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Proceedings - 2022 International conference on Innovation, Knowledge, and Management, ICIKM 2022

Proceedings - 2022 International Conference on Innovation, K...

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11th International conference on Innovation, Knowledge, and Management, ICIKM 2022

ISBN: (纸本)9781665461504

The proceedings contain 16 papers. The topics discussed include: research on the development and application of English for specific purposes corpora;study on the characteristics and prevention methods of seeper in mined-out area based on big data analysis and geophysical exploration technology;research on the decision support model of investment and financing based on bp neural network algorithm;research on the influence of visual merchandising on customer value in mobile recommendation system-based on the intermediary effect of brand image;analyzing and comparing national innovation system and regional innovation system literatures: a bibliometric study;research on literature evaluation method based on content analysis;knowledge and networking innovations: a mosaic;and the perspective of digital communication with intangible cultural heritage information under the media technology environment: in case study of Molan culture.

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36th International Florida Artificial Intelligence Research Society conference, FLAIRS-36 2023

36th International Florida Artificial Intelligence Research ...

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36th International Florida Artificial Intelligence Research Society conference, FLAIRS-36 2023

The proceedings contain 132 papers. The special focus in this conference is on Florida Artificial Intelligence Research Society. The topics include: Causanom: Anomaly Detection With Flexible Causal Graphs;answering Student Queries with a Supervised Memory Conversational Agent;topological data analysis in Natural Language Processing – A Tutorial;k-medianoids Clustering Algorithm;automatic Generation of Paragraph Templates Using a Textual Energy Approach: Applications in Literary Text Production;on Bounding the Behavior of a Neuron;semantics-based Framework for Incentivized Research data Sharing;considerations in the Design of Realistic Agents for Serious Games;impact of Artificial Intelligence Regulations on Organizational Risks;an Interactive Interpretation viewer for Typed First-order Logic;security of Text to Icon/Image conversions;Code Generation for Collectible Card Games with Complex APIs;a Study of Compressed Language Models in Social Media Domain;biogeography-based optimization for feature selection;malicious or Benign? Towards Effective Content Moderation for Children’s videos;a Multi-Dictionary Approach to Abstractness/Concreteness-Based Authorship Attribution;bayesian Network Conflict Detection for Normative Monitoring of Black-Box Systems;relative Performance of Bilateral Multiattribute Negotiation Strategies in Open Markets;relative Effects of Positive and Negative Explanations on Satisfaction and Performance in Human-Agent Teams;ARCHIMEDES: Active Reasoning Conducted with Heterogeneous Information Monitors for Evading Dangers and Extended Surveillance;increasing Fairness in Predictions Using Bias Parity Score Based Loss Function Regularization;visual Episodic Memory-based exploration;A Comparison of AutoML Hyperparameter Optimization Tools For Tabular data;DynamicG2B: Dynamic Node Classification with Layered Graph Neural Networks and BiLSTM;unsupervised Keyword Extraction for Hashtag Recommendation in Social Media;deep Separable Hypercomplex Networks.

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The Utility Impact of Differential Privacy on Credit Card data in Machine Learning Algorithms 10

The Utility Impact of Differential Privacy on Credit Card Da...

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10th International conference on Information Technology and Quantitative Management, ITQM 2023

作者： Luo, Xiaopeng Wang, Siyuan Chen, Haolong Luo, Zongwei Hong Kong Baptist University Kowloon Tong Hong Kong IRADS BNU-HKBU United International College Zhuhai China Faculty of Science and Technology BNU-HKBU United International College Zhuhai China BNU-UIC Institute of AI and Future Networks Beijing Normal University Zhuhai519000 China Artificial Intelligence and Data Science Research Hub BNU-HKBU United International College Zhuhai China

With the development of networks and financial technologies, credit card data is increasingly being used in various fields of data analysis such as user behavior, financial transactions, and market analysis. These fields often use multiple machine learning algorithms for data mining on credit card dataset. It is worth noting that credit card data contains more diverse and comprehensive information compared to traditional data, and the dataset may contain multiple data types. At the same time, credit card data may also expose users' privacy information. Differential privacy algorithms can add random noise to the data set, protecting sensitive information while ensuring certain data utility. However, there has been little research on the use of differential privacy algorithms on credit card data in multiple machine learning algorithms, and there has been insufficient exploration of the utility impact of differential privacy on complex credit card data. These research gaps exist in both the financial technology and privacy protection industries. Therefore, this paper applies differential privacy to credit card data in multiple classic machine learning algorithms, discusses the utility impact of various differential privacy algorithms on credit card data, and compares the performance of credit card data sets protected by differential privacy in different algorithms. © 2023 The Authors. Published by Elsevier B.v. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Tenth International conference on Information Technology and Quantitative Management.

关键词： data mining

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data-Driven Human Factors Enabled Digital Twin 49

Data-Driven Human Factors Enabled Digital Twin

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49th Annual conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Kolesnikov, Mikhail v. Atmojo, Udayanto Dwi vyatkin, valeriy Aalto University Department of Electrical Engineering and Automation Espoo Finland Lulea Tekniska Universitet Department of Computer Science Computer and Space Engineering Sweden

ISBN: (纸本)9798350331820

This paper presents a methodology for increasing human-centric production systems flexibility using human factors-enabled digital twins. The paper includes an analysis of the relevant projects that incorporate human-related data collection and processing. The proposed system is capable of collecting human factors-related data from various sources and then use a decision-making algorithm to schedule the tasks according to assessed human operator conditions in real-time. The formed Digital Twin is able to depict the condition of the labourer and production system status in real-time using visual Components simulation environment. Shown results prove that existing production systems are capable of adapting to the changing condition of the worker flexibly, optimising workflow, distributing tasks with AGvs and cobots, and applying changes in workplace ergonomics to achieve better safety and performance of the worker. © 2023 IEEE.

关键词： Decision making

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Self-Supervised visual Representation Learning Framework for Road Sign Classification 1

Self-Supervised Visual Representation Learning Framework for...

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2023 1st DMIHER International conference on Artificial Intelligence in Education and Industry 4.0, IDICAIEI 2023

作者： Dsilva, Godson Michael Ashok, vinayak Finolex Academy of Management and Technology Information Technology Department Ratnagiri India

ISBN: (纸本)9798350338423

In the landscape of machine learning, the dominance of supervised learning in crafting specialized models for specific tasks is evident, yet its confinement in terms of broader generalization has underscored the quest for more versatile models capable of acquiring multiple skills without an insurmountable reliance on labelled data. A pivotal avenue toward this goal lies in self-supervised learning (SSL), which promises to instill foundational knowledge and a semblance of common sense into AI systems. This investigation undertakes a comprehensive exploration of diverse SSL strategies, elucidating their inherent constraints on the road sign detection and classification usecase. Through an intricate comparative analysis spotlighting Context Autoencoders (CAE) and MoCov3 - two prominent state-of-the-art methodologies leveraging masked image modelling and contrastive learning, respectively - we unravel their distinct attributes and limitations. Furthermore, we introduce an innovative end-to-end framework meticulously tailored for self-supervised road sign classification. Our roadmap encompasses the infusion of domain-specific expertise in road signs into this framework, followed by rigorous benchmarking against state-of-the-art SSL models across diverse road sign datasets, employing a spectrum of evaluation metrics. This study emerges as a stepping stone, propelling AI closer to comprehesive intelligence by harnessing the potency of self-supervised paradigms. © 2023 IEEE.

关键词： Supervised learning

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RouteRover: AI-Enabled Traffic Congestion Prediction and Route Optimization for Indian Urbanites

RouteRover: AI-Enabled Traffic Congestion Prediction and Rou...

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Recent Advances in Science and Engineering Technology (ICRASET), International conference on

作者： S Naveen G Sanketh M G Jayanthi M P Shreya Prashanth Kannadaguli Artificial Intelligence and Machine Learning Cambridge Institute of technology Bengaluru India Computer science and engineering Cambridge Institute of technology Bengaluru India Dhaarini Academy of Technical Education Bengaluru India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

Here in this paper, a model that has been proposed for predicting and visually representing congestion of traffic using machine learning as well as deep learning. This initiative with the purpose of examining traffic data to obtain insights for traffic regulation and planning. The process initiates with an initial exploration of data through an Exploratory data analysis (EDA) and to perceive the distribution and patterns within the dataset. Descriptive statistics, correlation analysis, and visual aids are employed for this purpose. Following the EDA phase, tactics for manifold learning are utilized to simplify the dataset while retaining crucial information. Identifying significant features influencing the prediction of traffic volume is facilitated by methods for feature importance and selection. The obtained features are rigorously evaluated to comprehend traffic patterns and traffic jam levels. visual representations capture the distribution of traffic volume based on factors like time, day, and month, assisting in pinpointing congestion hotspots and optimizing traffic flow. This project underscores the powerful impact of data analysis and feature extraction in offering practical insights for traffic regulation strategies.

关键词： visualization data analysis Scalability Transportation Traffic control Predictive models Routing Regulation Real-time systems Traffic congestion

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Automatic Integration of While Drilling Cutting Descriptions with Formation Evaluation Models

Automatic Integration of While Drilling Cutting Descriptions...

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2023 SPE Annual Technical conference and Exhibition, ATCE 2023

作者： Alakeely, A.A. Torlov, v.S. Qubaisi, K.A. Kanfar, M.F. Saudi Aramco Dhahran Saudi Arabia

ISBN: (纸本)9781613999929

The procedure of analyzing and integrating text information from cutting descriptions collected during drilling process with formation evaluation models is a tedious task that is demanding in terms of time and human labor. The work describes a process to automate the task and use it to identify hydrocarbon zones in exploration wells. As the drill bit penetrates the formations in a well, the rock fragments are usually described across the associated depth of interest in a text format. These descriptions are valuable source of information that are usually compared to petrophysical models that uses wireline logs as input. In this work, textual cutting descriptions are utilized in identifying potential hydrocarbon zones using natural language processing and deep learning algorithms. The methodology uses natural language processing techniques to generate data set. The data set is used to train a deep learning algorithm to identify hydrocarbon zone from cutting descriptions automatically. The system has been used to identify hydrocarbon zones in a group of exploration wells. The trained model has been applied to blind test wells. The methodology reached hydrocarbon detection accuracy of 98% in challenging areas. For example, it allows hydrocarbon detection in low resistivity pay reservoirs where Archie's based saturation calculations present a water wet zone. The result was confirmed by formation tester and testing results suggesting the correctness of the technique. Moreover, the automation reduces the turnover time of such analysis and reduces human error. The enclosed process helps in integrating textual information into the petrophysical model to reduce uncertainty, human labor, and aid in identifying hydrocarbon zones. In addition, the method will reduce the need for unnecessary testing, resulting in cost saving in rig time operation. Copyright © 2023, Society of Petroleum Engineers.

关键词： Hydrocarbons

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exploration of Cultural IP Image and Common Pattern Gene Extraction in virtual Reality Design Interaction 16th

Exploration of Cultural IP Image and Common Pattern Gene Ext...

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16th International conference on virtual, Augmented and Mixed Reality (vAMR)

作者： Zhou, Junyu Chen, Ruiyang Nanjing Normal Univ Dept Visual Commun Nanjing 210098 Peoples R China Nanjing Normal Univ Art Educ Major Nanjing 210098 Peoples R China

ISBN: (纸本)9783031610400;9783031610417

This paper first studies the origin and flow of IP images from the interactive perspective of design, explores the definition and rules of IP in the form of knowledge graph based on semiotics, and puts this problem into the background of the current wave of AI and virtual reality, and raises the following questions: - The definition of IP in virtual reality and the original IP in the network? What is the way forward? Based on the AI data era background of IP business image for induction automation design processor, to achieve better IP cultural communication and exchange. The author selected the female ethnic characteristic clothing of Hui 'an area of Fujian Province as the representative object of design. In this realization part, the article first takes Hui 'an women's ethnic clothing as an example to explore the characteristics of ethnic clothing induction methods - structure multi-modal form and cross-comparison analysis of horizontal multi-cultural pattern design and extract the elements. Then, according to the formula points summarized, the virtual IP image is designed with the characteristic ethnic dress of women in Hui 'an area of Fujian Province, China. According to the cultural gene and regional characteristics contained in the regional ethnic image, the pattern factors are decomposed reconstructed, and applied to the IP image design under the meta-structure virtual reality technology. On this basis, the vR system of the minority cultural IP generator is built. It allows users to generate IP images of featured nationalities in a more immersive environment through shapes, colors, lines, etc., summarized by patterns. Finally, the team tested the vR system. According to the five aspects of "whether the generated IP image is more vivid/whether it can integrate national characteristics/whether it is more immersive/whether it is more memorable/whether it is easier to understand", a total of 20 members participated in the evaluation. In the answer, we finally fin

关键词： IP image design National characteristic pattern finishing virtual reality interaction

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Big data analysis based intrusion detection in WSN with reduced features

Big Data analysis based intrusion detection in WSN with redu...

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2023 International conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering, ICECONF 2023

作者： Dharini, N. Priya, D.M. Sruthi Sneghaa, v.A. Sakthi Easwari Engineering College Dept of E.C.E Chennai India

ISBN: (纸本)9798350334357

Energy and Security remain two of the biggest obstacles that Wireless Sensor Networks (WSNs) must overcome. Protecting WSNs from Denial of Service (DoS) attacks are some of the security challenges associated with WSNs. The Intrusion Detection System (IDS) should guarantee the security of the WSN services. This IDS must be able to recognize as many security risks as it can and be compatible with WSN features. In this paper, intruder node detection is accomplished using various machine learning approaches. Our work focuses on Big data analysis based attack detection in WSN with the reduced dataset. In this work, we utilized the WSN - DS dataset. To increase classification accuracy and reduce processing complexity, feature selection is done on the dataset and a reduced dataset is created. Flooding, Blackhole, Grayhole, and TDMA attacks are the four forms of DoS attacks that are taken into consideration in this work. The parameters used to assess the attack detection are the training time to build the machine learning model, and the number of Instances that are Correctly- Classified and Incorrectly- Classified. The outcomes demonstrate that Random forest outperforms other classifiers with a high accuracy rate of 98.17% for the reduced dataset. The Bagging classifier takes less time to train the model than Random forest as well as gives an accuracy of 98% for the reduced dataset. © 2023 IEEE.

关键词： Denial-of-service attack

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Effect analysis on Field Acquisition Parameters of Surface Wave Survey - Case Study on the Gobi Desert Field 10

Effect Analysis on Field Acquisition Parameters of Surface W...

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10th International conference on Environmental and Engineering Geophysics, ICEEG 2023

作者： Ren, Quanchao Li, Zhongsheng Liu, Sitong Tian, Awei Li, Yongxin Xiao, Weiguo Lu, Qiang Su, Wuque Li, Xingwang School of Geology Engineering and Geomatics Chang'An University Xi'an China Northwest Institute of Nuclear Technology Xi'an China The 2nd Gas Production Plant of North China Petroleum Bureau SINOPEC Zhengzhou China Changqing Oilfield Company No.1 Oil Production Plant PetroChina Yan'an China

In this paper, the Gobi Desert landform is used as a test site to analysis the results on surface wave survey. The body wave exploration methods are used to determined velocity structure of the site as a reference standard firstly. Subsequently, different acquisition parameters for surface wave are tested. After comparing the results, the optimal parameters are selected by using the surface wave multi-channel analysis method (MASW). Meanwhile, several theoretical equations for estimating minimum offset, optimum offset, geophone spread length, and resolution of dispersion are demonstrated as the guidelines to organize the real-world data acquisition. Finally, the selected optimal parameters are applied to the data acquisition of total 69 surface wave recorders. The final two-dimensional inversion profile is in good agreement with the known background. It means the method of MASW is feasible in geological surveys on Gobi Desert landform. © Published under licence by IOP Publishing Ltd.

关键词： Surface waves

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