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New Version of Fast Automatic Determination of Cluster Numbers for High Dimensional Big Data

New Version of Fast Automatic Determination of Cluster Numbe...

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2022 International Conference on Computational science and Computational Intelligence, CSCI 2022

作者： Safari, Zohreh Elizabeth City State University Dept. Computer Science Math Engineerign Technology Elizabeth City United States

ISBN: (纸本)9798350320282

The main issue in clustering algorithms is how to efficiently define number of clusters automatically. Considering both the quality of clustering and efficiency of clustering algorithm during determination of number of clusters can be a trade off that was our main purpose to overcome with. Successfully, In our approach the best number of clusters for a large data set of high dimensional data automatically would be determined with respect to clustering quality and efficiency. We carried out experimental studies on our five previous data sets [43]. and 4 new larger ones by which we found that our procedure has the flexibility of choosing different criteria to determine the optimal K under each of them. The procedure takes the advantages of Bisecting K-Means algorithm, and indicated higher efficiency compared with Ray & Turi method when it comes to find the best number of clusters. © 2022 IEEE.

关键词： Big data

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Revolutionizing Campus Exploration with GikiLenS: A Deep Learning-Powered Object Detection App 18

Revolutionizing Campus Exploration with GikiLenS: A Deep Lea...

引用

18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Mueed, Iqra Arshad, Usama Ali, Raja Hashim Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

For newcomers and tourists, navigating university campuses can be difficult, resulting in aggravation and lost time. We respond by introducing 'GikiLenS', an object identification application driven by deep learning that revolutionizes campus exploration by accurately identifying buildings and landmarks while enhancing user experience. GikiLenS is a comprehensive and user-friendly smartphone application that precisely caters to the demands of newcomers and visitors, unlike previous studies in campus navigation. Our study intends to close this gap and develop a reliable method for identifying campus buildings, optimizing navigation, and enhancing user experience. Through rigorous testing, GikiLenS has proven to have remarkable real-time building detection accuracy, highlighting its potential as an important tool for campus exploration. The app's outcomes and conclusions demonstrate how well it works to give users specific building information, promoting a more knowledgeable and enjoyable campus experience. The importance of our findings lies in the development of a unique, user-centered app that offers a cutting-edge method of campus mobility. Our contribution consists of a game-changing method for building detection that improves campus exploration and user pleasure. © 2023 IEEE.

关键词： Object detection

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Design and implementation of a scoring system for computer image recognition technology 12

Design and implementation of a scoring system for computer i...

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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Chang, Cheng Fanjiang, Yong-Yi Hung, Chi-Huang Wang, Li-Feng Chang, Ya-Jou Chang, Wen-Hsuan Fu Jen Catholic University Graduate Institute of Applied Science and Engineering New Taipei City Taiwan Fu Jen Catholic University Graduate Institute of Applied Science and Engineering Dept. of Computer Science and Information Engineering New Taipei City Taiwan Fu Jen Catholic University Lee-Ming Institute of Technology Graduate Institute of Applied Science and Engineering Dept. of Information Technology New Taipei City Taiwan Tatung University Lee Ming Institute of Technology Graduate Institute of Design Science New Taipei City Taiwan Lee-Ming Institute of Technology Dept. of Applied Cosmetology New Taipei City Taiwan

ISBN: (纸本)9798350340181

This paper presents a scoring system for evaluating makeup paper pictures using computer vision feature recognition. In traditional makeup scoring systems, the assessment is conducted by human reviewers, leading to potential subjective biases and raising concerns about scoring fairness. To address this issue, the paper proposes a solution that utilizes computer vision feature recognition technology for scoring. By leveraging various computer vision techniques such as structural feature matching and image recognition retrieval, it develops a method for predicting makeup styles and scores based on an image learning model. An experimental platform is designed, utilizing paper drawings created by students and scores provided by multiple evaluators as makeup image data, to automatically generate grading results. This research demonstrates the potential of computer vision feature recognition technology to enhance scoring accuracy and objectivity in the future. © 2023 IEEE.

关键词： Grading

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Diving into Brain Complexity: Exploring Functional and Effective Connectivity Networks 18

Diving into Brain Complexity: Exploring Functional and Effec...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Siddique, Abu Bakar Khalid, Hassan Bin Ali, Raja Hashim University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan

ISBN: (纸本)9798350328172

The human brain's intricate functions are under-pinned by a vast network of synapses that enable chemical impulses between neurons. Neuroscientists employ two key approaches, functional and effective connectivity, to understand the brain's complexity, focusing on its operations, cognition, and behavior. While both methods utilize graph theory for network analysis, functional connectivity, which investigates associated brain activity, has seen more substantial research efforts than effective connectivity, which examines information processing's inter-regional impacts. In this research paper, we aim to present an extensive examination of the emerging discipline that combines graph theory with the analysis of brain characteristics, shedding light on how these characteristics arise from the interactions among different groups of neurons. Our primary focus lies in exploring the diverse cognitive and neurological applications that leverage functional Magnetic Resonance Imaging (fMRI) as a tool. Additionally, we offer a comprehensive overview of the methods employed to construct brain networks based on functional and efficient connections. Throughout the discussion, we emphasize the advantages and limitations associated with these approaches. © 2023 IEEE.

关键词： Graph theory

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Efficient Models for Detecting Monkeypox using Skin Lesion Images 3

Efficient Models for Detecting Monkeypox using Skin Lesion I...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Sunda, Neelam Sharma, Divya Battu, Sujith Banoth, Rewanth Nayak Rahul Monika Kanoria PG Mahila Mahavidyalaya Department of Computer Science Jaipur India Kanoria PG Mahila Mahavidhyalaya Department of Computer Science jaipur India Delhi Technological University Dept. of Software Engineering New Delhi India Delhi Technological University Department of Applied Physics New Delhi India University of Delhi Shaheed Rajguru College of Applied Sciences for Women Department of Computer Science India

ISBN: (纸本)9798350343632

Monkeypox is predicted to be the next pandemic and is spreading over African countries like water under a mat. The study basically investigates three deep learning imaging models for the identification of monkeypox from skin lesion photos from the MSID dataset. The motto is to define a computer-based technique to identify monkeypox from just its image, making it cheaper and faster with accurate results. The study achieved the highest accuracy of 99.37% as compared to previous works related to Resnet 50. The limits and complexity of the procedures in real-world situations are also clearly addressed. © 2023 IEEE.

关键词： Convolutional Neural Network Image preprocessing Monkeypox Performance Evaluation Metrics Transfer learning

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An Iterated Local Search Algorithm for Commuting Bus Routing Problem with Latest Arrival Time Constraint

An Iterated Local Search Algorithm for Commuting Bus Routing...

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2022 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022

作者： Hashimoto, H. Hu, Y. Sugiura, T. Takada, Y. Yagiura, M. Tokyo University of Marine Science and Technology Dept. Logistics and Information Engineering Tokyo Japan Tokyo University of Science Dept. Applied Mathematics Tokyo Japan Nagoya University Graduate School of Information Science Dept. Computer Science and Mathematical Informatics Nagoya Japan

ISBN: (数字)9781665486873

ISBN: (纸本)9781665486873

Given a set of bus stops and a set of employees, the commuting bus routing problem with latest arrival time constraint (CBRP-LATC) aims to determine routes of buses to carry every employee from the company to one of the bus stops at which he/she wants to get off. Each employee walks to his/her destination after getting off the bus and has a desired time when he/she wants to arrive there, which is called the latest arrival time. The objective is to find a set of routes with the minimum total traveling cost that covers all employees' destinations under bus capacity and latest arrival time constraints. We propose an iterated local search algorithm in which we use two kinds of neighborhood operations called set-del/1-ins and VRP-OPT∗. The set-del/1ins operation consists of three neighborhood operations, setdel, 1-ins and set-del-ins to remove or assign employees from or to a bus route. The VRP-OPT∗ is a local search algorithm to improve the distances of routes based on four neighborhood operations, 2-opt, 2-opt∗, relocate and cross-exchange. Computational results show the effectiveness of the two neighborhood operations. © 2022 IEEE.

关键词： Personnel

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Explainable RNN Classification of Right Ventricular Dysfunction from Echocardiogram Reports

Explainable RNN Classification of Right Ventricular Dysfunct...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Rajaratanam, Arun Corizzo, Roberto Boukouvalas, Zois Mid-Atlantic Permanente Group AlexandriaVA United States American University Department of Computer Science WashingtonDC United States American University Dept. of Math and Statistics WashingtonDC United States

ISBN: (纸本)9798350362480

Accurately identifying patients with Right Ventricular Dysfunction (RVD) is critical for timely diagnosis and treatment, yet it remains a significant challenge in clinical practice due to the complexity and variability of echocardiography text reports. In this study, we address this crucial problem by evaluating the effectiveness of recurrent neural networks (RNNs) in classifying patients with and without RVD from echocardiography text reports. We demonstrate that our model exhibits strong performance, with high recall and precision in identifying RVD, underscoring its potential utility in clinical settings. Furthermore, we enhance the interpretability of the framework by leveraging the Local Interpretable Model-Agnostic Explanations (LIME) framework. This approach allows us to extract and validate meaningful semantic information related to RVD, providing deeper insights into the model's local predictive capabilities and improving transparency in decision-making. © 2024 IEEE.

关键词： Patient treatment

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Enhancing Customer Experience, and Brand Awareness through Familiar Gameplay in Advergames

Enhancing Customer Experience, and Brand Awareness through F...

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2022 IEEE Asia-Pacific Conference on computer science and Data Engineering, CSDE 2022

作者： Khezrian, Navid Farzinnejad, Forouzan Baghaei, Nilufar Coburg University of Applied Sciences Dept. Electrical Engineering and Computer Science Coburg Germany University of Queensland Dept. Information Technology and Electrical Engineering Brisbane Australia

ISBN: (纸本)9781665453059

The marketing field has been looking for more creative and effective ways to attract customers. Advergames have recently attracted a lot of attention in increasing brand awareness and attracting wider groups of audiences. Research studies have indicated that customers have a more positive attitude toward a brand and stronger purchase intentions when playing advergames with brand interactivity rather than without brand interactivity. Despite the increased use of advergames as a brand promotion strategy by a range of well-known brands, there is still a limited understanding of several issues related to the effective use of such games. In this paper, we conducted one study (n= 242) held in one exhibition to examine familiar gameplay in advergames and its effectiveness in attracting and engaging more people. Our results showed that the presence of familiar gameplay in advergames is an essential feature in attracting more people, enhancing their user experience, and increasing brand awareness. © 2022 IEEE.

关键词： Sales

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Ship Data Clustering for Improved Fuel Efficiency Optimization Decision Support Systems

Ship Data Clustering for Improved Fuel Efficiency Optimizati...

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OCEANS 2024 - Halifax, OCEANS 2024

作者： Piercey, Caitlin Hamilton, Matthew Veitch, Brian Barnes, Joshua Jiang, Xianta Memorial University of Newfoundland Dept. of Computer Science St. John's Canada Memorial University of Newfoundland Dept. of Engineering and Applied Sciences St. John's Canada Ocean Coastal and River Engineering National Research Council Canada St. John's Canada

ISBN: (纸本)9798331540081

In this paper, we propose a hybrid clustering algorithm based on hierarchical clustering in combination with string clustering, similar to that used in clustering genetic sequences, to extract clusters of operational activities from ship data. After extracting clusters (n=9), the distribution and summary statistics of each cluster are compared to Canadian Coast Guard provided operational definitions to obtain context from the clusters. The clustered data is used as the input to a fuel flow prediction model, which will in turn form the basis of an on-board decision support system for marine vessels with the goal of reducing fuel consumption. Finally, we compare the results of fuel flow predictions with models trained on a) different clusters and b) the full dataset, to determine the usefulness of these clusters in improving the performance of a fuel-optimization decision support system for on-board marine vessel use. © 2024 IEEE.

关键词： Ships

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Design and Implement Driveway Door Access Control by Using VLC and RFID Technology 12

Design and Implement Driveway Door Access Control by Using V...

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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Hung, Chi-Huang Fanjiang, Yong-Yi Li, Cheng-Yen Chiu, Sheng-Wei Hung, Tzu-Chieh Chiang, Meng-Huan Fu Jen Catholic University Lee-Ming Institute of Technology Graduate Institute of Applied Science and Engineering Dept. of Information Technology New Taipei City Taiwan Fu Jen Catholic University Graduate Institute of Applied Science and Engineering Dept. of Computer Science and Information Engineering New Taipei City Taiwan Fu Jen Catholic University Graduate Institute of Applied Science and Engineering New Taipei City Taiwan Lee-Ming Institute of Technology Dept. of Vehicle Engineering New Taipei City Taiwan Kang Chiao International School Dept. of International High School New Taipei City Taiwan

ISBN: (纸本)9798350340181

In recent years, the application of radiofrequency identification technology in smart access control systems has become a trend. Users only need to take out the RFID electronic key and approach the sensor of the gate to open the gate through signal induction. For housewives who always have a lot of bags and bags in their hands, there is no need to free up one hand to find the key to open the door and avoid being in a hurry. However, it is relatively inconvenient for drivers. Instead, it is much more convenient to use wireless controllers or license plate recognition. This study proposes a system that combines RFID with optical communication technology and vehicle headlights to open the garage door with a light code. © 2023 IEEE.

关键词： Access control

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