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Development of a Robot System for Cleaning Sludge in Industrial Site 1

10th International Conference on Robot Intelligence Technology and Applications, RiTA 2022

作者： Hur, J.G. Lim, Chang Gyoon Kim, Yong Min Shinjeong Development Co. Ltd. 27 Bonggye 2-gil Yeosu59631 Korea Republic of Department of Computer Engineering Chonnam National University Yeosu59626 Korea Republic of Major in Electronic Commerce Chonnam National University Yeosu59626 Korea Republic of

ISBN: (数字)9783031268892

ISBN: (纸本)9783031268885

Recently, the development of unmanned robots for work support that can minimize the risk factors of workers in harsh work and special environments where access is difficult is being actively carried out in the industrial field. This development is a cleaning robot that is put in to discharge the sludge accumulated on the bottom of the pollutant storage tank in the wastewater tank or sewage tank to the outside. In supporting the work, this robot is applied with obstacle avoidance technology that turns obstacles during path movement, as well as a function of following a path to specify or predict a work path for autonomous work. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Robots

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Generative AI for Radiological Image Data: Current Trends and Outlook

Generative AI for Radiological Image Data: Current Trends an...

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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Ji-Won Han Yeon-Joon Lee Department of Applied Artificial Intelligence Major in Bio Artificial Intelligence Hanyang University Seoul South Korea Department of Computer Science and Engineering Major in Bio Artificial Intelligence Hanyang University Seoul South Korea

Generative artificial intelligence (AI) has gained prominence, particularly in the field of radiological image data. This study investigates current trends and prospects of generative AI in X-ray, CT, MRI, and PET imaging. Emphasizing the importance of data quantity and quality, we ensure reliable and useful outputs. Given the significance of ethical dimensions in generative AI for radiological imaging, ethical considerations are also addressed. By analyzing recent advancements, this study offers insights into the evolution of generative AI in radiological image data and future directions.

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Using Artificial Intelligence and Deep Learning Methods to Analysis the Marketing Analytics and Its Impact on Human Resource Management Systems 5th

Using Artificial Intelligence and Deep Learning Methods to A...

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5th International Conference on Emerging Technologies in computer engineering: Cognitive Computing and Intelligent IoT, ICETCE 2022

作者： Gambhir, Vinima Asnate-Salazar, Edwin Prithi, M. Alvarado-Tolentino, Joseph Tongkachok, Korakod SVIMS Business School Wadala Maharashtra Mumbai India Computer Science and Engineering Universidad Nacional Santiago Antunez de Mayolo Huaraz Peru Computer Science Periyar University Tamil Nadu Vellore India Science and Engineering with a Major in Audit and Computer Security Universidad Nacional Santiago Antunez de Mayolo Huaraz Peru Faculty of Law Thaksin University Songkhla Thailand

ISBN: (纸本)9783031070112

In recent years, all firms have been concerned with marketing analytics. They’re using numerous advanced technologies to analyse marketing analytics. Artificial intelligence (AI) and Deep Learning (DL) technology are highly capable of examining large databases for patterns and insights. These technologies enable the marketing function to encompass its reach and analytics empower a deeper understanding of how the market responds to actions. With the help of Deep learning, businesses are now able to connect a wide range of datasets to better understand what customers want with greater sophistication and analytic capacity, and then use that information to gain a competitive advantage. In addition, Deep learning uses numerous technical tools that excel at extracting perceptions and patterns from huge amounts of data and then predicting the future for marketing. Thus, Deep learning can potentially be used to create products that are tailored to what customers want. It has been found that marketing analytics play a significant role in HRM. It helps in examining the employees’ skill sets and developing a training programme based on the market demands. AI assists firms in determining target audiences and devising a strategy to meet its objectives. Also, AI technology adapts and learns from data to make data-driven decisions. Many time-consuming and managerial chores will be automated by HR software that includes artificial intelligence. A lot of administrative activities are automated and speeded up using AI. Moreover, to gain in-depth knowledge of Artificial Intelligence and Deep learning this paper is conducted. Furthermore, the article examines how artificial intelligence and deep learning technologies are utilised to assess marketing statistics, as well as their impact on human resource management systems. For this paper, descriptive research methodology has been used for this study, and secondary data has been used to obtain reliable conclusions. © 2022, The Author(s),

关键词： Marketing

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Person Name Extraction from TV program Using Pre-trained Language Model and News Headline 10

Person Name Extraction from TV program Using Pre-trained Lan...

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10th International Congress on Advanced Applied Informatics, IIAI-AAI 2021

作者： Oda, Kazuki Sasaki, Minoru Major in Computer and Information Sciences Graduate School of Science and Engineering Ibaraki University Ibaraki Hitachi Japan Ibaraki University Faculty of Engineering Dept. of Computer and Information Sciences Ibaraki Hitachi Japan

ISBN: (纸本)9781665424202

In this study, we focus on extracting person names from text contained in from TV programs and TV-CMs broadcast in Japan. Person name extraction from TV programs is a challenging task because person names are often recognized with other entity names such as locations and organizations. To tackle this problem, we experiment with an existing named entity extraction method using a list of names extracted from Wikipedia. However, this method results in low precision in extracting the person names, which leads to the problem that the list of person names is not effective for TV program texts. In this paper, to solve this problem, we propose a person name extraction method using Conditional Random Fields (CRF) with ELMo pre-training language model. Also, we propose to use news headlines to construct the person name extraction model for effective extraction of person names performing on the TV programs. As a result of our experiments, the proposed model trained with training data and news headlines provides the highest precision. These results show that adding the news headlines as external information to TV program data is effective for person name extraction. © 2021 IEEE.

关键词： Extraction

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Research on Mining Association Rules for Classroom Concentration Based on Apriori Algorithm

Research on Mining Association Rules for Classroom Concentra...

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IEEE Information Technology and Mechatronics engineering Conference (ITOEC)

作者： Jingqiu Bai Huafeng Kong Wenhan Hu Department of Information Engineering Wuhan Business University Hubei China Major in Data Science and Big Data Technology The Main Research Direction is the Computer Application

This paper studies the correlation between students' concentration in class and learning interest, emotional state and other influencing factors. By collecting students' classroom status data, a data set suitable for data mining is constructed. Based on the idea of Apriori algorithm, the Anaconda Navigator programming tool is used to mine the association rules of students' classroom concentration. Through data mining based on Apriori algorithm, and the association rules between students' emotional state, learning interest and other factors and in-class test scores are output. The results of strong association rules show that the better students' emotional state, learning interest and other states are, the better their classroom concentration performance is. Mining the potential influencing factors of students' classroom concentration provides valuable reference theory for students to improve classroom concentration and teachers to improve classroom teaching effect, which is helpful for teachers and students to build a more efficient classroom together in the future.

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Differentially Private Synthetic Adversarial Network Traffic Generation Based on Tabular Diffusion Processes

Differentially Private Synthetic Adversarial Network Traffic...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Minjae Kang Gunhee Cho Sungju Yun Yeonjoon Lee Department of Computer Science and Engineering Major in Bio-Artificial Intelligence Hanyang University Ansan Republic of Korea Department of Intelligent Information Convergence Engineering Hanyang University Ansan Republic of Korea College of Computing Hanyang University Ansan Republic of Korea

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

IDS is becoming increasingly important as networks become more vulnerable to a variety of attacks. However, traditional ML and DL-based IDS have limited accuracy and false positive rates due to the lack of open adversarial network datasets and vendors' concerns regarding potential data leakage during the training process. To address these challenges, VAE and GAN-based network packet generation models have emerged, but they still face issues with low data fidelity. In this context, TabDDPM is a suitable option for generating network packets, as it surpasses VAE and GAN in accurately capturing data distribution characteristics and effectively learning the inherent structure of network packet formats. Despite its potential, research applying TabDDPM to network packet generation has not yet been explored. In this paper, we propose and evaluate DP-NetDDPM, a novel framework that enables adversarial network traffic generation while preserving both fidelity and privacy guarantees. Specifically, we examine the use of diffusion to generate adversarial network packet datasets and compare its performance with baseline models. Our framework focuses on three key criteria: data fidelity, adaptability for machine learning applications, and data privacy. The results show that DP-NetDDPM surpasses traditional models in both fidelity and adaptability, achieving a notable 72% improvement in fidelity and a 45% enhancement in adaptability over baselines.

关键词： Training Adaptation models Privacy Telecommunication traffic Machine learning Network security Generative adversarial networks Data models Faces Payloads

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Multi-Class Classification of Lung Cancer Using Machine Learning and Image Processing

Multi-Class Classification of Lung Cancer Using Machine Lear...

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Computing (ICOCO), IEEE International Conference on

作者： Teerapat Charnsripinyo Naruemon Wattanapongsakorn Mathematics and Sciences Major Trium Udom Suksa School Bangkok Thailand Department of Computer Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand

In this research paper, we present lung cancer classification using machine learning techniques. We used several techniques to classify the CT-scan images into three classes which are normal, benign and malignant (cancerous) classes. The dataset was obtained from a reliable source having 1096 images overall. The dataset was preprocessed to have balanced classes and feature extracted using multiple image filters. Many experimental cases were conducted using various machine learning techniques which are decision tree, random forest, support vector machine and K-nearest neighbor, and evaluated considering binary class classification and multi-class classification, which are normal vs. non-normal, cancerous vs. non-cancerous, and normal vs. benign vs. cancerous. Five performance metrices which are confusion matrix, accuracy, recall, precision and F1-score were considered. The results show that the random forest and K-nearest neighbor techniques that we consider performed very well. The normal class (healthy subjects) and the benign (not-harmful tumor) class can be correctly classified 97%-99%. Overall, we obtained the accuracy of 99.73 % for 2-class classification, and accuracy of 98.69 % for the 3-class classification.

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Occluded-based Facial Recognition under mask using Deep learning method 3

Occluded-based Facial Recognition under mask using Deep lear...

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3rd International Conference on computer Vision, Image and Deep Learning and International Conference on computer engineering and Applications, CVIDL and ICCEA 2022

作者： Wang, Haoyu Wei, Guanhua Xu, Haoning Zhang, Chong Computer Science and Technology Guangzhou Huashang College Guangzhou511300 China Guilin University of Electronic Technology Major in Network Engineering Beihai536000 China Hainan Tropical Ocean University Computer Science and Technology Sanya572022 China Tiangong University Information Management and Information System Tianjin300387 China

ISBN: (纸本)9781665459112

At present, under the epidemic prevention and control, face recognition is facing the problem of masks. The traditional face recognition system dataset is composed of a face without mask pictures, and when the mask wearers need to do the face recognition, they have to take off the mask, which makes the epidemic situation environment increase the burden of prevention and control. Therefore, effectively improving the efficiency of face recognition under face masking is still a difficult point in the recognition system. In this paper, we aim to conduct facial mask detection to detect the person with or without the mask. Three steps are included in our method, including facial detection, adding mask and facial detection with mask. To achieve this goal, we aim to use MTCNN and FaceNet as the main components of our method. Our method can achieve 76.42% accuracy among all the data. The results on both real and generated dataset validate the effectiveness of out method. © 2022 IEEE.

关键词： Face recognition

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Development of Augmented Reality-based Dental Surgical Guide System for Clinical Use

Development of Augmented Reality-based Dental Surgical Guide...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Yusuke Fukuda Akira Takebayashi Hiroaki Yamamoto Toru Kano Dept. Major in Computer and Information Sience Ibaraki University Ibaraki Japan Medical Engineering Lab. Saitama Japan Tsuda Dental Clinic Ibaraki Japan Dept. of Mechanical Systems Engineering Ibaraki University Ibaraki Japan

ISBN: (数字)9798350355079

ISBN: (纸本)9798350355086

Surgical guide plates, which are used in dental implant treatments, have some limitations that cause problems for the operator and patient. This study aims to develop an augmented-reality-based surgical guide system to solve these problems and discusses its use in clinical practice. Our surgical guide system achieved a good accuracy in drilling simulations on training typodonts, demonstrating its potential in clinical practice.

关键词： Training Drilling Electric potential Accuracy Surgery Implants Dentistry Consumer electronics

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Meta-Explore: Exploratory Hierarchical Vision-and-Language Navigation Using Scene Object Spectrum Grounding

arXiv

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arXiv 2023年

作者： Hwang, Minyoung Jeong, Jaeyeon Kim, Minsoo Oh, Yoonseon Oh, Songhwai Electrical and Computer Engineering and ASRI Seoul National University Korea Republic of Department of Electronic Engineering Hanyang University Korea Republic of Interdisciplinary Major in Artificial Intelligence Seoul National University Korea Republic of

The main challenge in vision-and-language navigation (VLN) is how to understand natural-language instructions in an unseen environment. The main limitation of conventional VLN algorithms is that if an action is mistaken, the agent fails to follow the instructions or explores unnecessary regions, leading the agent to an irrecoverable path. To tackle this problem, we propose Meta-Explore, a hierarchical navigation method deploying an exploitation policy to correct misled recent actions. We show that an exploitation policy, which moves the agent toward a well-chosen local goal among unvisited but observable states, outperforms a method which moves the agent to a previously visited state. We also highlight the demand for imagining regretful explorations with semantically meaningful clues. The key to our approach is understanding the object placements around the agent in spectral-domain. Specifically, we present a novel visual representation, called scene object spectrum (SOS), which performs category-wise 2D Fourier transform of detected objects. Combining exploitation policy and SOS features, the agent can correct its path by choosing a promising local goal. We evaluate our method in three VLN benchmarks: R2R, SOON, and REVERIE. Meta-Explore outperforms other baselines and shows significant generalization performance. In addition, local goal search using the proposed spectral-domain SOS features significantly improves the success rate by 17.1%andSPLby 20.6%againstthe state-of-the-art method of the SOON benchmark. Project page: https://***/projects/Meta-Explore/*** © 2023, CC BY-NC-SA.

关键词： Navigation

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