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Design of College Students' Practice and learning Resources Recommendation System Based on Big data Technology 11

Design of College Students' Practice and Learning Resources ...

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11th international conference on Logistics and Systems engineering

作者： Wu, Xiaojuan College of Education Nanchang Institute of Science and Technology Jiangxi Nanchang330108 China

ISBN: (纸本)9781921712791

improving the practice and learning ability of college students needs various support, and it is very necessary to develop a set of Resource Recommendation System Based on big data technology. the system can help students efficiently obtain practice and learning resources, and teachers can further understand students' learning through the system. therefore, combined with the characteristics of contemporary college students' learning, this paper plans to introduce the slope one algorithm into the user-based collaborative filtering algorithm to solve the cold start problem caused by sparse scoring data in the system, and then develop and design a practical learning resource recommendation system suitable for college students to serve the practical education system of "multiple interaction". © Aussino Academic Publishing House All rights reserved.

关键词： Recommender systems

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Depth and Width Adaption of DNN for data Stream Classification with Concept Drifts 24th

Depth and Width Adaption of DNN for Data Stream Classificati...

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24th international conference on intelligent data engineering and automated learning, IDEAL 2023

作者： Zhou, XingZhi Liu, Xiang Wen, YiMin Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9783031482311

To handle data stream classification with concept drifts, recent studies have shown that a continuously evolving network structure can achieve better performance. However, firstly, they only change one hidden node at a time, which is not enough to alleviate underfitting or overfitting of network model, leading to model’s inability to fit data well. Secondly, during the growth process of the network, they did not consider reducing hidden layers, which would affect the learning ability of deep neural network models. To overcome these shortcomings, an adaptive neural network structure (ANSN) is proposed to handle data stream classification with concept drifts. ANSN has a completely open structure, its network structure, depth, and width can evolve automatically in online mode. the experimental results on ten popular data stream datasets show that the proposed ANSN outperforms the comparison methods. the codes of the proposed algorithm is available on https://***/ymw12345/***. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Deep neural networks

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Fifth international conference on Artificial Intelligence and Computer Science, AICS 2023

Fifth International Conference on Artificial Intelligence an...

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5th international conference on Artificial Intelligence and Computer Science, AICS 2023

ISBN: (纸本)9781510668621

the proceedings contain 143 papers. the topics discussed include: lithology identification algorithm of cuttings based on one-dimensional convolutional neural network for mineral element content data;research on rock image classification algorithm based on multi-task learning and pseudo-labels;research on intelligent recognition algorithm for common roadbed diseases based on radar images;pre-planning of individualized talus implants based on computed tomography-automated segmentation;mining order-preserving submatrix based on charm for gene expression data;an improved watershed algorithm and its application in image edge extraction;research on augmented reality registration method based on SA-PSO algorithm;a personalized test paper generation model for English examination based on knowledge tracking;and improved ACLPSO algorithm for automatic parameter optimization.

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the Behavioral and Geographical Features Based Machine-learning Method for Alerting the Drivers 11th

The Behavioral and Geographical Features Based Machine-Learn...

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11th international conference on intelligent Computing and Applications, ICRTC 2023

作者： Singh, Kuwar Pratap Kumar, Ashwani Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Delhi-NCR Campus Delhi-Meerut Road Modi Nagar U.P Ghaziabad India

ISBN: (纸本)9789819717231

Lack of attention due to drowsiness is a leading cause of vehicle accidents. Drowsiness is a significant risk factor in the breakdown of many people’s lives. the most excellent approach to prevent drowsy driving accidents is to alert the drivers in advance. As a result, there is a requirement for a real-time system that can identify drowsiness early and accurately. In this paper, we proposed the behavioral and geographical features-based machine learning method (BGF-MLM) to identify sleepiness at the earlier stage to detect the driver’s drowsiness. A sizable realistic dataset is incorporated throughout three states of consciousness: attentive, decreased vigilance, and drowsiness. the video utilized fall into two extreme categories: awake and drowsy. Computer vision and deep learning methods retrieve behavioral and geographical features. Behavioral features are extracted using an artificial neural network (ANN) and fed into a long short-term memory (LSTM) network. the geographical features are extracted using an LSTM network. the suggested method details the processes involved in developing a drowsiness detection system to estimate a driver’s tiredness level and issue an alarm before the driver poses a significant risk to traffic safety. If the driver is warned in time, they can escape several possible disasters. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Long short-term memory

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Implementation of A Deep learning Framework for intelligent Intrusion Detection in Internet of things Networks 5

Implementation of A Deep Learning Framework for Intelligent ...

引用

5th international conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Jeyapriyanga, S. Ravi, C.N. Rathiya, R. Kalaivani, K. Rama Devi, C. Kumar, Kallakunta Ravi St.Joseph's Institute of Technology Department of Information Technology Chennai India Cmr Engineering College Computer Science and Engineering Department Telangana Hyderabad India Information Technology Dr. Ngp Institute of Technology Coimbatore India School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India St.Joseph's College of Engineering Electrical and Electronics Engineering India Koneru Lakshmaiah Education Foundation Department of Ece AP Vaddeswaram India

ISBN: (纸本)9798350321425

Internet of things (IoT) has ushered in a new age that benefits humanity greatly. From automated healthcare to energy and transportation, the Internet of things covers many subject topics. IoT devices are vulnerable to several cyberattacks due to their limited resources. As data output has expanded exponentially from a zettabyte to a trillion per year, computer and IoT network expansion has surged. Network growth has created new security issues. In such large data sets, intrusions are hard to spot. Today's networks are used for 'smart' dwellings and cities, grids, devices, objects, online commerce, banking, government, etc. Due to internet data privacy and security concerns, Intrusion Detection Systems (IDS) have proliferated. Privacy, security, and resilience would suffer if IDS protection fails. Traditional defences can't handle modern attacks. Cutting-edge deep learning methods may automatically detect intrusions and network anomalies. this study's main goal is to evaluate intrusion detection using deep learning algorithms and compare the outcomes to other systems. this research work has proposed a novel deep-learning-based IoT invasion detection method. the cutting-edge Internet of things dataset contains IoT traces and real-world attack traffic including DoS, DDoS, data harvesting, and theft attempts. Finally, all publicly available network-based IDS datasets are analyzed in this research study. the accuracy, false alarm rate, recall, precision, f-1 score, and detection rate of many deep learning IDS algorithms have been examined. Furthermore, this research study has investigated the network security and privacy issues and solutions. © 2023 IEEE.

关键词： Internet of things

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Infrared Fuzzy Video data Cleaning Based on Improved YOLOv5 11th

Infrared Fuzzy Video Data Cleaning Based on Improved YOLOv5

引用

11th China conference on Command and Control, C2 2023

作者： Gong, Hua Tian, Haoyuan Chen, Congang Hu, Yutong School of Science Shenyang Ligong University Shenyang110159 China Liaoning Key Laboratory of Intelligent Optimization and Control for Ordnance Industry Shenyang110159 China Faculty of Science and Technology Beijing Normal University—Hong Kong Baptist University United International College Zhuhai519087 China National Key Laboratory of Electromagnetic Space Security Tianjing300308 China

ISBN: (纸本)9789819990207

Aiming at the problem that infrared fuzzy video data affect video quality, an improved YOLO v5 deep network model is established to carry out intelligent and accurate infrared fuzzy video data cleaning. Considering the problem of missing infrared fuzzy video data, a dataset named SYLU-INFRARED VIDEO dataSET is constructed. this dataset includes 5000 aerial and ground background infrared fuzzy video frames. Considering the spatiotemporal joint features of video data, a CBAM attention mechanism with two-dimensional attention allocation strategies is applied. this attention can automatically allocate the importance of video features extracted by YOLOv5 backbone in both spatial and channel dimensions. the experimental results show that the fusion model of CBAM and YOLO v5 enhances the cleaning effectiveness. CBAM-YOLOv5 has an increase of 17.8%, 28.4%, and 26.5% in Precision, Recall, and mAP compared to YOLOv7, respectively. © Chinese Institute of Command and Control 2024.

关键词： Antennas

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Searching for Short M-Dwarf Flares by Machine learning Method 11th

Searching for Short M-Dwarf Flares by Machine Learning Metho...

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11th international conference on Big data Analytics in Astronomy, Science, and engineering (BDA)

作者： Jiang, Hanchun Univ Tokyo 7-3-1 Hongo Tokyo Japan

ISBN: (纸本)9783031585012;9783031585029

We propose a machine learning method to identify M-dwarf flares in astronomical observation data. A flare is a sudden increase of luminosity of a star's surface, and is thought to be the result of magnetic reconnection. Observations of the stellar flares play a crucial role in understanding stellar magnetic activity. In particular, analyzing flare time evolution (light curve) is essential. We use the data from Tomo-e Gozen camera, mounted on the Kiso Schmit telescope, with a cadence of approximately one second, which is shorter than the cadence of other telescopes such as NASA's Kepler space telescope and the Transiting Exoplanet Survey Satellite. the dataset is ideal for identifying fast flares. We develop a one-dimensional convolutional neural network (CNN) to detect fast and faint flares in optical light curves. We train the model on a limited number of real flares identified by human experts, augmented with a large number of artificially generated flares to detect sub-minute flare candidates within the light curves captured by Tomo-e Gozen camera, and subsequently fit these candidates to make the selections. Our novel CNN model has successfully identified potential flares characterized by a rise time in the range of 4 s less than or similar to t(rise) less than or similar to 88 s, and energy levels spanning 10(30) erg less than or similar to E-flare less than or similar to 10(33) erg. Notably these potential flares exhibit shorter duration and lower energy compared to those detected by human experts, who typically identify flares with a rise time of 5 s less than or similar to t(rise) less than or similar to 100 s and energy of 10(31) erg less than or similar to E-flare less than or similar to 10(34) erg.

关键词： M dwarf star Stellar flare Photometric One-dimensional convolutional neural network

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Multi-View Fusion Networks for Multimodal Fake News Detection 11

Multi-View Fusion Networks for Multimodal Fake News Detectio...

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11th Joint international Information Technology and Artificial Intelligence conference, ITAIC 2023

作者： Cui, Wei Li, Pu Zhang, Xuerui College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Chongqing Institute of Green and Intelligent Technology Chongqing Key Laboratory of Big Data and Intelligent Computing Chinese Academy of Sciences Chongqing China

ISBN: (纸本)9798350333664

the proliferation of multimedia fake news misleads social opinion, damages social harmony, and seriously challenges the authority of news media. Current multimodal fake news detection methods focus on extracting better unimodal features using pre-trained models and developing complex networks for visual and textual feature fusion, mostly ignoring social contextual information. In the text, we present a novel Multi-View Fusion Network (MVFN) for fake news detection, which attempts to achieve fusion learning of heterogeneous multimodal features. the model not only explores cross-modal graph interactions between visual regions and sentence words, but also investigates the effect of social context on feature learning. In addition, we consider the alignment relationship of features in both perspectives for better feature fusion. Experimental results on Chinese and English datasets show that the proposed model outperforms current multimodal detection approaches. © 2023 IEEE.

关键词： fake news graph multi view multimodal

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Flamingo Search Algorithm with Optimal Deep learning for automated Cyber threat Detection in IoT Environments 4

Flamingo Search Algorithm with Optimal Deep Learning for Aut...

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4th international conference on Distributed Sensing and intelligent Systems, ICDSIS 2023

作者： Asiri, Mashael M. Department of computer Science College of Science & Art at Mohayel King Khaled University Abha Saudi Arabia

ISBN: (纸本)9781839539954

Internet of things (IoT) networks are prone to cyber attacks because of insecure wireless infrastructures, various kinds of IoT devices, large quantities of data transfer by sensors over the network and resource constraint architecture. Hence, IoT-compatible cybersecurity solutions are needed. One of the typical solutions to find cyber threats in IoT- based networks is an automatic detection system. the new trend in cyber security is Machine learning (ML)-based analytics intends at extracting security-related data to identify advanced target cyber threats and reduce the overhead of keeping static correlation rules. However, many prevailing solutions for cyber threat detection include numerous problems like high false positive rate (FPR), low scalability, high learning complexity, and poor accuracy. therefore, this study designs a new Flamingo Search Algorithm with Optimal Deep learning for automated Cyber threat Detection (FSAODL-ACTD) technique in the IoT system. the main objective of the FSAODL-ACTD technique is to automate identification of the cyberthreats in IoT networks. In the FSAODL-ACTD method, a new feature selection approach using FSA is derived. Besides, multilayer bidirectional long short term memory (MBiLSTM) model is utilized for the automated identification of the cyber threats in the IoT environment. Finally, sine cosine algorithm (SCA) is exploited for the optimal hyperparameter adjustment of the MBiLSTM method. the simulation outcomes of the FSAODL-ACTD method are tested on the cyberthreat dataset, and the outcomes ensured the supremacy of the FSAODL-ACTD approach over other current approaches. © the Institution of engineering & Technology 2023.

关键词： Cyber attacks

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Exploration and Design of intelligent Connected Vehicle Monitoring and Supervision Platform Based on Multi-Source data 11

Exploration and Design of Intelligent Connected Vehicle Moni...

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11th Joint international Information Technology and Artificial Intelligence conference, ITAIC 2023

作者： Dong, Shaoshuai Hu, Jianyao Wang, Xunji School of Automotive and Transportation Engineering Hefei University of Technology Hefei China School of Mechanical Engineering Southwest Jiaotong University Chengdu China The Fifth Electronics Research Institute of Ministry of Industry and Information Technology Guangzhou China Hefei Intelligent and Connected Vehicles Innovation Center Hefei China

ISBN: (纸本)9798350333664

With the rapid advancement of intelligent Connected Vehicle (ICV), the demand for monitoring and supervision of vehicles has become increasingly urgent. To address this need, this paper presents a design for an ICV monitoring and regulation platform that leverages multi-source data. By gathering information from on-board sensors, traffic surveillance cameras, and road monitoring devices, such as traffic participant data, vehicle information, traffic conditions and surrounding environmental data, this platform can integrate multi-source data to achieve real-time monitoring and analysis of ICV behavior, safety and operational status. It presents a comprehensive view of road conditions and the overall functioning of the ICV, helping managers to monitor ICV operations from a large to a small scale. By engaging with the ICV, this platform enables management and regulation of the ICV, thereby improving operational efficiency and safety. Additionally, it provides precise analysis and optimization suggestions for vehicle operating status, assisting management personnel in understanding vehicle performance and capabilities. the design of this platform offers a comprehensive and efficient solution for ICV monitoring and supervision, providing robust support for the advancement of intelligent Transport Systems (ITS) and the widespread adoption of ICV. © 2023 IEEE.

关键词： artificial intelligence ICV multi-source data visualization module

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