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Multi-Class Network Intrusion Detection Using Deep Neural Networks Tuned on Imbalanced data 56

Multi-Class Network Intrusion Detection Using Deep Neural Ne...

引用

56th international Carnahan conference on Security technology, ICCST 2023

作者： Tyagi, Shruti Pingulkar, Shriya Shaikh, Amaan Computer Engineering K.J. Somaiya College of Engineering Mumbai India Information Technology K.J. Somaiya College of Engineering Mumbai India

ISBN: (纸本)9798350315875

As computer networks become ubiquitous, they face escalating risks from cyberattacks that can severely disrupt critical infrastructure. Existing network intrusion detection datasets have limitations like inadequate diversity of samples and poor classification accuracy. this research utilizes the University of Nevada - Reno Intrusion Detection dataset (UNR-IDD) encompassing normal traffic and 5 different intrusion types to develop a deep neural network for multi-class intrusion detection. After exploratory data analysis, a baseline Multi-Layer Perceptron model was developed. A structured tuning methodology involving ANOVA feature selection and grid search hyperparameter optimization is followed to improve validation performance. the research quantitatively demonstrates the impact of methodical tuning guided by validation accuracy, establishing a template for developing high-performance deep learning intrusion detection systems. With further enhancements, the techniques explored could equip security experts with invaluable intelligence to protect networks against sophisticated cyber threats © 2023 IEEE.

关键词： Principal component analysis

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An AI Based Approach for Classification and Performance Evaluation of Teaching Assistant 5

An AI Based Approach for Classification and Performance Eval...

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5th international conference on Contemporary Computing and Informatics, IC3I 2022

作者： Malik, Varun Mittal, Ruchi Singh, Vikram Rana, Ajay Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Chaudhary Devilal University Sirsa India Engineering and Technology Amity University Uttar Pardesh India

ISBN: (纸本)9798350398267

In order to test and evaluate the performance of various machine learning classifiers for teaching assistant classification, this research will use machine learning approaches. the teaching assistant dataset was taken from the Kaggle data store. After preprocessing the data to remove any inconsistencies, many supervised learning techniques were used, and their efficacy was assessed. the outcomes show that for the teaching assessment models, the ensemble technique performs better than the conventional classification techniques. © 2022 IEEE.

关键词： Supervised learning

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UGENet: Learning Discriminative Embeddings for Unconstrained Gaze Estimation Network via Self-Attention Mechanism in Human-Computer Interaction 24

UGENet: Learning Discriminative Embeddings for Unconstrained...

引用

5th international conference on Computer Information and big data Applications, CIBDA 2024

作者： Liu, Hai Song, Yu Liu, Tingting Ju, Jianping Tang, Jianying School of Information and Artificial Intelligence Nanchang Institute of Science and Technology Nanchang330108 China National Engineering Research Center for E-Learning Central China Normal University Wuhan430079 China

ISBN: (纸本)9798400718106

Gaze estimation plays an important role for indicating the human behavior and intention. It has developed rapidly with the field of computer vision, enabling it to be used in a variety of applications, such as virtual reality and human-computer interaction. However, gaze estimation in the field is still challenging due to different head postures, lighting conditions and eye appearances. In this work, we propose a novel gaze estimation model with framework based on ResNet-50, which improves its estimation accuracy. In addition, the same loss function for each predicted angle is used to improve the generalization performance of the model. Finally, the proposed model is validated on the two most unconstrained popular datasets, such as MPIIGAZE and GAZE360. the experimental results show the proposed approach performs better compared to other better methods. © 2024 ACM.

关键词： Computer vision

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Research on the Integrated Construction technology of Building engineering Practice Based on MACO's YOLOv5 Target Detection Algorithm 7

Research on the Integrated Construction Technology of Buildi...

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7th international conference on Vision, Image and Signal Processing, ICVISP 2023

作者： Wang, Lingling Li, Xue College of Construction Engineering Shandong University of Engineering and Vocational Technology Shandong Province Jinan City China

ISBN: (纸本)9781839539848

With the development of the construction field in the building and engineering industry, the technique of integration of engineering practices has gained attention. the algorithms that constitute the technique are simple, so the conventional algorithms have a small adaptation range. In order to increase the adaptive range of the algorithm, this study adds pheromones to the ant colony of the ant algorithm and combines it with the You Only Look Once v5 algorithm to generate a fusion algorithm suitable for target detection in construction engineering practice. Finally, the study conducted experiments using data from the Const dataset, along with experiments with algorithms such as Extreme Gradient Boosting, to validate the superiority of the fusion algorithm. the clarity of the fusion algorithm is 97, while the other three algorithms are 95, 93 and 86;the scanning distances of the four algorithms are 592 m, 514, 405 and 241 m. the experimental results show that the fusion algorithm proposed by the study extracts the highest quality images, has the widest recognition distances, and is suitable to be applied in integrated construction technology in architectural engineering practice. © the Institution of engineering & technology 2023.

关键词： Ant colony optimization

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RETIA: Relation-Entity Twin-Interact Aggregation for Temporal Knowledge Graph Extrapolation 39

RETIA: Relation-Entity Twin-Interact Aggregation for Tempora...

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39th IEEE international conference on data engineering, ICDE 2023

作者： Liu, Kangzheng Zhao, Feng Xu, Guandong Wang, Xianzhi Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan China University of Technology Sydney Data Science and Machine Intelligence Lab Sydney Australia

ISBN: (纸本)9798350322279

Temporal knowledge graph (TKG) extrapolation aims to predict future unknown events (facts) based on historical information, and has attracted considerable attention due to its great practical significance. Accurate representations (embeddings) of entities and relations form the basis of TKG extrapolation. Recent work has been devoted to improving the rationality of entity representations. However, on the one hand, ignoring relation modeling results in incomplete relation representations;therefore, some approaches aggregate only immediately adjacent entities of relations, but this can lead to the "message islands"problem of relation modeling. On the other hand, ignoring the association constraints between relations and entities can make the embeddings of both relations and entities prone to overfitting. To address the abovementioned challenges, we propose an advanced method, namely, RETIA. For the former issue, we generate twin hyperrelation subgraphs for each historical subgraph and then aggregate both the adjacent entities and relations in the hyperrelation subgraphs through a graph convolutional network (GCN). About the latter concern, we propose a twin-interact module (TIM), which provides communication channels for relation aggregation and entity aggregation during the evolution of the historical sequence. Experiments conducted on five public datasets show that RETIA has made great improvements across several evaluation metrics. Our released code is available at https://***/CGCL-codes/RETIA. © 2023 IEEE.

关键词： Extrapolation

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Large Language Models for Marketing Research: A Survey and New Perspectives 58

Large Language Models for Marketing Research: A Survey and N...

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58th Hawaii international conference on System Sciences, HICSS 2025

作者： Qian, Yang Chen, Shaoge Wu, Chenyang Yuan, Kun Du, Yanan Jiang, Yuanchun Liu, Yezheng School of Management Hefei University of Technology China School of Health Service Management Anhui Medical University China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Anhui Hefei230009 China

ISBN: (纸本)9780998133188

Large Language Models (LLMs) have brought unprecedented innovation opportunities to the marketing field. However, the practical applications of LLMs within the marketing landscape currently exhibit a fragmented and scattered nature. In this study, we aim to aggregate these scattered literature to create a holistic view of LLMs capabilities for marketing research. Specifically, we present an overview of LLMs using the evolution of LMs. Subsequently, we explore their application in the marketing domain across five distinct dimensions: data annotation, idea inspiration and content generation, substitution of human participants, user behavior learning and prediction, and evaluation of LLM feedback. Finally, we discuss the new trends and challenges for LLMs in marketing. this study enriches the theoretical foundations of integrating generative AI with marketing practices. © 2025 IEEE Computer Society. All rights reserved.

关键词： Market Research

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Hand-Gesture Based Emoji Identification and Chat Integration 5

Hand-Gesture Based Emoji Identification and Chat Integration

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

作者： Tare, Sanskar S Patil, Madhav J Alam, Adnan Swarnalatha, P. School of Computer Science and Engineering Vellore Institute of Technology Vellore Vellore India

ISBN: (纸本)9798350321425

Every aspect of human life is slowly getting computerized and gradually turning electronic. this has made a rangeof tasks a lot easier and accessible for people from all walks of life. Unfortunately, this rapid computerization and the ever changing landscape of electronics has given birth to a new classof computer - illiterate individuals. Such individuals exist due to the lack of accessibility to latest electronic gadgets, or humansocial problems like old age. there is a need to make technology fun and easier to use for individuals belonging to such classes. the very primary use of internet-based networking is text-based communications namely: e-mails, texts chats, blogs, etc. the text based interaction is mainly facilitated using a keyboard which makes it the most focal human-computer interaction involved. the modern usage of this text-based communication involves extensive usage of emoticons. While most handheld devices feature a customised emoji-keyboard, the emoji keyboard is difficult to access in personal computers. this can adversely affect the interactions the aforementioned computer-illiterates partake in. this project aims to deal with this problem by creating a hand-detection based emoji detection algorithm in python through OpenCV and Mediapipe for detection of hand landmarks and implementing it in one such communication channel. the algorithm consists of two modules: a Hand-Tracking Moduleand an emoji detector capable of matching the tracked hand to existing emoji database. the resulting application is consistent, allow frequent users to use shortcuts and make the user feel more in control. the work also strives to integrate error prevention and simple error handling in existing emoji detection technologies along with managing short term memory load. © 2023 IEEE.

关键词： Palmprint recognition

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Discovering Potential Founders Based on Academic Background 15

Discovering Potential Founders Based on Academic Background

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15th international conference on Knowledge Management and Information Systems, KMIS 2023 as part of the 15th international Joint conference on Knowledge Discovery, Knowledge engineering and Knowledge Management, IC3K 2023

作者： Arzani, Arman Handte, Marcus Zella, Matteo Marrón, Pedro José University of Duisburg-Essen Essen Germany Niederrhein University of Applied Sciences Krefeld Germany

ISBN: (纸本)9789897586712

technology transfer is central to the development of an iconic entrepreneurial university. Academic science has become increasingly entrepreneurial, not only through industry connections for research support or transfer of technology but also in its inner dynamic. To foster knowledge transfer, many universities undergo a scouting process by their innovation coaches. the goal is to find staff members and students, who have the knowledge, expertise and the potential to found startups by transforming their research results into a product. Since there is no systematic approach to measure the innovation potential of university members based on their academic activities, the scouting process is typically subjective and relies heavily on the experience of the innovation coaches. In this paper, we study the discovery of potential founders to support the scouting process using a data-driven approach. We create a novel data set by integrating the founder profiles with the academic activities from 8 universities across 5 countries. We explain the process of data integration as well as feature engineering. Finally by applying machine learning methods, we investigate the classification accurracy of founders based on their academic background. Our analysis shows that using a Random Forest (RF), it is possible to successfully differentiate founders and non-founders. Additionally, this accuracy of the classification task remains mostly stable when applying a RF trained on one university to another, suggesting the existence of a generic founder profile. Copyright © 2023 by SCITEPRESS - Science and technology Publications, Lda.

关键词： data integration

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An Automatic Navigation System for New Technical Advanced Drones for Different Alpplications 5

An Automatic Navigation System for New Technical Advanced Dr...

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5th IEEE international conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Talwandi, Navjot Singh thakur, Payal Khare, Shanu Chandigarh University Dept of Computer Science Engineering Punjab India

ISBN: (纸本)9798350383522

Autonomous pilot technology integrated into drones has ushered in a new era of unmanned aircraft systems, creating a multitude of applications this abstract examines the importance of the importance and potential of autonomous pilot aircraft, underscoring its transformative impact on industries ranging from agriculture to surveillance and beyond Navigation requires intelligent a unmanned aerial vehicles (UAVs) will be equipped with sophisticated artificial and sensor systems, enabling them to operate autonomously without constant human intervention A rapidly growing field in robotics and aerospace technology Tires It is showing to do so, the paper provides a comprehensive overview, including key concepts, challenges, and recent developments in autonomous automation enter the box. It also discusses, and educates, the critical importance of real-time operations, the critical role of obstacle detection and avoidance systems, and the use of GPS and optical-based navigation delves into how machine learning and artificial intelligence enhance the autonomy of drones. In addition, the paper explores the many potential applications and benefits of autonomous drone communications, from search and rescue missions to precision agricultural delivery Growing interest and investment in this technology in 2013 highlights its potential to transform industries, promising a future marked by improved safety and performance. © 2024 IEEE.

关键词： and applications artificial intelligence Autonomous drones GPS machine learning navigation obstacle avoidance real-time data processing vision-based navigation

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Simulation of Pedestrian Lane Changing Behavior in Asymmetric OD Scenario 8

Simulation of Pedestrian Lane Changing Behavior in Asymmetri...

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8th international conference on Electromechanical Control technology and Transportation, ICECTT 2023

作者： Cheng, Zhiyang Yue, Hao Lin, Yuzhu You, Jiepeng Key Laboratory of Integrated Transportation Big Data Application Technology for Transportation Industry Beijing Jiaotong University Beijing100044 China China Railway Construction Southern Investment Co. Ltd. Chengdu610200 China

ISBN: (纸本)9781510668331

In the process of walking, pedestrians usually have a certain destination. However, the current velocity of the pedestrian is not constantly consistent with the destination. thus the pedestrian needs to adjust the current velocity by changing lanes. In this study, based on different velocities of reaching the destination, the deviation coefficient is introduced to represent the deviation of the pedestrian's current velocity from the destination;It is also proposed that the pedestrian has inertia for adjustments of the current velocity and would tolerate a certain degree of deviation out of walking comfort. thus the pedestrian's lane-changing action is transformed into a dynamic balance between maintaining the current velocity and adjusting the velocity for the destination. In the end, a velocity-based pedestrian lane-changing simulation model is proposed, and a single pedestrian walking scene is simulated to obtain the smooth walking trajectory, in which the phenomenon of hysteresis for lane-changing can be clearly observed. © 2023 SPIE.

关键词： Scene simulation Transportation data integration Decision making Industrial applications Industry Reflection Tolerancing

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