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2nd IEEE International Conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication engineering, ICATIECE 2022

作者： Kumar, V Sunil Sowmyashree, S. Pareek, Piyush Kumar Khanna, Ashish Costa De Albuquerque, Victor Hugo Gupta, Deepak Nitte Meenakshi Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India Computer Science & Engineering East West Institute of Technology Bengaluru India Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Cearά Fortaleza Brazil Maharaja Agrasen Institute of Technology GGSIPU CSE Dept India Federal University of Cearά Department of Teleinformatics Engineering Brazil

ISBN: (纸本)9781665493963

It is essential to develop an efficient Client Relationship Model in order to make use of the estimations about customer turnover (CRM). Finding and breaking down the facts surrounding the business information understanding is necessary for client produce prediction, which should be made possible effectively by altering the strategies used for business insight. The tools that are provided by company insight allow for the historical, current, and prospective perspectives of business activity to be anticipated and dissected. It is possible to deal with it in an efficient way by providing the information mining approaches that choose the data from the created informative index that is the most helpful. Fundamental leadership should be possible to achieve in a fruitful way if one has this information. The actions that consumers have taken in the recent past are going to be analyzed, and an accurate forecast is going to be formed about those customers who are likely going to get unhappy in the near future based on the results of that prediction. The problem is that deciding on a certain period to investigate the client's activities and also establishing on a specific day and age for each of the clients won't be appropriate6. This is due to the fact that the problem is caused by the fact that deciding on a certain period to investigate the client's activities will take too long. The challenge lies just in this aspect. How to decide on a certain amount of time to spend researching the activities of the customer. For instance, when planning a model, 100 customers are used, 70 of those customers are considered 'active,' which indicates that they are still conducting business with the organisation, and the remaining 30 customers are considered 'agitate,' which indicates that they have severed their ties with the organisation. The primary purpose of the work that has been suggested is to carry out exploratory research and management as a multicriteria basic leadership issue, a

关键词： Forecasting

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Practical Implementation of Q-Learning and Object Detection for Mobile Robot Path Planning

Practical Implementation of Q-Learning and Object Detection ...

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International Conference on Advanced Robotics and Intelligent Systems (ARIS)

作者： John P. Valcourt Franya M. Chandler Chamma Avrelus Jou-Yi Lee Ali Ihsan Gullu Syed H. Shah Dept. of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan International Bachelor Program in ECE Yuan Ze University Taoyuan Taiwan Mechanicai/Industrial Engineering DE Yuan Ze University Taoyuan Taiwan International Bachelor Program in Engineering Yuan Ze University Taoyuan Taiwan Defense Electronics Engineering Gebze Technical University Kocaeli Turkiye

ISBN: (数字)9798350362572

ISBN: (纸本)9798350362589

Expanding on an earlier study that assessed the performance of a Q-learning approach for solving the path planning problem for mobile robots, this research implemented the RL approach in a real-world setting employing the RoboMas-ter EP Core. The robotic system also included object detection and recognition through the robot's sensors and a pre-trained YOLOv9 model. The robot navigated to predefined target points while avoiding stationary obstacles. The Q-learning algorithm was trained using the Google Colaboratory platform. Experi-ments conducted at various speeds identified an optimal speed and high success rates in obstacle avoidance and target region accuracy were achieved. Additionally, the object detection sys-tem demonstrated strong performance in real-time applications. Despite these successes, challenges such as high friction and multitasking inefficiencies were identified. Future research should address these limitations by enhancing control systems and the robot's multitasking capabilities, as well as using a computer with better processing power to improve overall system performance further.

关键词： Q-learning Accuracy Navigation Friction Object detection Robot sensing systems Multitasking

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A Hybrid Rolling and Flying Robot for Pipeline Fault Detection Using Deep Learning

A Hybrid Rolling and Flying Robot for Pipeline Fault Detecti...

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Advances in science and engineering Technology International Conferences (ASET)

作者： Megan Ghaly Rohan Mitra Abdullah Al Rayess Assem Ahmed Dara Varam Mohammad A. Jaradat Michel Pasquier Ahmed Khalil Department of Mechanical Engineering American University of Sharjah Sharjah UAE Department of Computer Science and Engineering American University of Sharjah Sharjah UAE Mechatronics Engineering Gradute Program American University of Sharjah Sharjah UAE

The Middle Eastern and North African region is highly reliant on the oil and gas industry. Subsequently, the need for pipeline inspection and fault diagnosis has become paramount. Current inspection methods rely on manual procedures that introduce sources of error into the results. In this paper, the design for a hybrid rolling and flying unmanned aerial vehicle (UAV) for pipeline inspection is proposed. The UAV was developed with a rolling frame capable of landing on pipelines to externally inspect them for cracks, leaks, or faults by traversing along the pipeline's surface while maintaining its stability. The hybrid UAV uses a camera to classify cracks along the surface of the pipeline using deep learning models. The paper then tests several models and deploys the best model found during testing -MobileNet100 along with the proposed design for the UAV. Finally, the prototype that was presented in this paper has been tested within a controlled environment to verify the compliance of the flying, rolling and fault detection subsystems.

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Learner Action Patterns in the Problem-Solving Process Related to program Code Composition Based on Tracking System Activities 7

Learner Action Patterns in the Problem-Solving Process Relat...

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7th International Conference on Informatics and Computing, ICIC 2022

作者： Syahidi, Aulia Akhrian Supianto, Ahmad Afif Hirashima, Tsukasa Watanobe, Yutaka Politeknik Negeri Banjarmasin Smart City Information System Applied Undergraduate Program Banjarmasin City Indonesia Research Center for Data and Information Sciences Bandung City Indonesia Graduate School of Advanced Science and Engineering Hiroshima University Kagamiyama Learning Engineering Laboratory Higashihiroshima Japan University of Aizu Division of Information Systems Department of Computer Science and Engineering Fukushima AizuWakamatsu Japan

ISBN: (纸本)9798350345711

Learning activities are an indicator of the learner's desire to learn during the learning process. The pattern of learner action is related to learning activities. In this case, in extracting the learning process, it is necessary to collect a lot of data through analysis of the learning process. The purpose of this study is to recommend and report the performance of an activity tracking system equipped with visual artifacts as an educational data mining approach to analyze action patterns when learners complete the arrangement of program code lines in each programming problem. In this study, data were taken from activity recordings when learners used interactive learning media for a basic programming subject called TOLSYASUPI-EduMed which had problem-solving learning models embedded in their interactions. Learning Analytics is used as a method of this research and data relationships from learner actions during the learning process using the 'if-then' rule. Log data is used for recording and detecting activities carried out by learners. We confirm that the results of the study are based on the findings of the data relationship that if the lines of program code are too long (more than five lines) then almost all learners experience a bottleneck condition. The completion time of each question and the number of iterations in completing the program code lines for each question have also been recorded and have a pattern of connection between the data. The information that has been obtained is then forwarded to teachers or stakeholders as key information to make appropriate feedback to learners based on the results as a means of analyzing and evaluating the programming learning process. © 2022 IEEE.

关键词： Data mining

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Entanglement-Enhanced Neyman-Pearson Target Detection

arXiv

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arXiv 2024年

作者： Ward, William Hariri, Abdulkarim Zhang, Zheshen Applied Physics Program University of Michigan Ann ArborMI48104 United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48104 United States

Quantum illumination (QI) provides entanglement-enabled target-detection enhancement, despite operating in an entanglement-breaking environment. Existing experimental studies of QI have utilized a Bayesian approach, assuming that the target is equally likely to be present or absent before detection, to demonstrate an advantage over classical target detection. However, such a premise breaks down in practical operational scenarios in which the prior probability is unknown, thereby hindering QI's applicability to real-world target-detection scenarios. In this work, we adopt the Neyman-Pearson criterion in lieu of the error probability for equally likely target absence or presence as our figure of merit for QI. We demonstrate an unconditional quantum advantage over the optimal classical-illumination protocol as benchmarked by the receiver operating characteristic, which examines detection probability versus false-alarm probability without resorting to known prior probabilities. Our work represents a critical advancement in adapting quantum-enhanced sensing to practical operational settings. © 2024, CC BY.

关键词： Quantum entanglement

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Predicting Human Activity with LSTM Face Detection on Server Surveillance System

Predicting Human Activity with LSTM Face Detection on Server...

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

作者： Alexander Nurenie Yaya Heryadi Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Indonesia

Surveillance server technology was growth with new technology, effective, extra new features, human friendly, and human deals with big amount data, can't view and collect the data in the short time, and took time to analyze, playback video/picture to determine machine, human, vehicle or environment issues or performance, Surveillance Server Systems now which has the ability to face recognition, face detection, human detection, motion detection, license plate recognition, The authors perform this study that still new this research has never been done before to determine the efficacy of the LSTM in predicting human behavior (Long Short Term Memory) Face Detection on Server surveillance system, by taking log view data with a total of 91501 Face detection data downloaded from 10/18/2022~11/9/2022, the data will be processed using Python programming and training so that it can be used to predict the future regarding human activities that vary utilizing time series prediction LSTM include the number of daily activities, the highest and lowest numbers of days, and the maximum and minimum numbers of days. from the results of this study it was found to help to find out the days with the lowest number of humans and the days with the highest number of human activities, so that the owner can predict with sequence of the data the service would be provided when human activity is high in certain area or certain day, it can also can find out the maximum or minimum amount human counting day by day, and compare able some different date and location, the author will continue to do more in-depth research the others data related with prediction with deep learning server surveillance machine system interaction with human, vehicle behavior in the future studies.

关键词： Training Surveillance Time series analysis programming Behavioral sciences Servers Face detection

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Iterative Morphological Training Set Decomposition for Endoscopic Tool Segmentation

Iterative Morphological Training Set Decomposition for Endos...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Yicheng Zhu Xiaoyi Wu Sylvia Tan Cuiling Sun Sulagna Saha Yun-Hsuan Su Kevin Huang Picker Engineering Program 100 Green Street Smith College Northampton MA USA Department of Computer Science 50 College St Mount Holyoke College South Hadley MA USA

ISBN: (数字)9798350355369

ISBN: (纸本)9798350355376

This paper proposes a modified method for training tool segmentation networks for endoscopic images by parsing training images into two disjoint sets: one for rectangular representations of endoscopic images and one for polar. Previous work [1], [2] demonstrated that certain endoscopic images may be better segmented by a U-Net network trained on the original rectangular representation of images alone, and others performed better with polar representations. This work extends that observation to the training images and seeks to intelligently decompose the aggregate training data into disjoint image sets — one ideal for training a network to segment original, rectangular endoscopic images and the other for training a polar segmentation network. The training set decomposition consists of three stages: (1) initial data split and models, (2) image reallocation and transition mechanisms with retraining, and (3) evaluation. In (2), two separate frameworks for parsing polar vs. rectangular training images were investigated, with three switching metrics utilized in both. Experiments comparatively evaluated the segmentation performance (via Sørenson Dice coefficient) of the in-group and out-of-group images between the set-decomposed models. Results are encouraging, showing improved aggregate in-group Dice scores as well as image sets trending towards convergence.

关键词： Training Measurement Image segmentation Minimally invasive surgery Mechatronics Aggregates Training data

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Multicast Routing Technique for Augmenting Routing Efficiency in Mobile WSN

Multicast Routing Technique for Augmenting Routing Efficienc...

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International Conference on Computing Methodologies and Communication (ICCMC)

作者： K. Anand M. Rajkumar Srikanth MS S. Radhika Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai Tamil Nadu India School of Information Technology and Engineering VIT University Vellore Tamil Nadu India Program Specialist TNS India Foundation Bangalore Karnataka India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

Mobile Wireless Sensor Network (MWSN) is an active network completely self-governing from a fixed structure. Also, MWSN has small channel bandwidth, great node movement, and inadequate battery energy. In WSN, flooding raises the reliability regarding successful communication of a packet with greater overhead. The flooding consumes the network's resources quickly, especially in terms of the lifetime of the node and energy, etc. Unicast routing creates an extra overhead and increases energy consumption because unicast routing mainly focuses on only minimum distance. But, multicasting is used when the desired destination is at maximum distance and increases the network's reliability in terms of throughput. This paper's objectives are to develop an efficient and reliable protocol using multicasting and unicasting to overcome the issue of higher overhead due to flooding. Multicast Routing Technique for Augmenting (MRTA) routing efficiency in MWSN is introduced. This method aims to enhance the link stability of the route for raising the route lifetime and diminishing the routing load. This method discovers the stable path from several ways to decline the overhead and delay between the sender and receiver. Simulation results demonstrate that this approach improves the network lifetime increases the throughput, and minimizes the routing load in the MWSN.

关键词： Wireless sensor networks Unicast Receivers Multicast communication Routing Throughput Stability analysis

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Topic Relation Graph Construction to Identify Challenges in Introducing programming in Indonesian K-12 Education

Topic Relation Graph Construction to Identify Challenges in ...

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Smart Technology (ICE-SMARTec), IEEE International Conference on

作者： Mewati Ayub Oscar Karnalim Hapnes Toba Maresha Caroline Wijanto Hans Dulimarta Williyandie Saputra Tabitha Emanuella Kotambunan Master Program of Computer Science Maranatha Christian University Bandung Indonesia Dept. of Computer Science and Engineering Ewha Womans University Seoul South Korea School of Computing Grand Valley State University Michigan USA Informatics Engineering Maranatha Christian University Bandung Indonesia

programming can help K-12 students to develop their 21st-century core skills. Despite the benefits, programming is not common to be delivered in Indonesian K-12 education. There is a need to understand potential challenges in introducing programming to K-12 students. We developed a questionnaire survey covering four identified dimensions of challenges: administrative, facilities, teachers, and students. We also asked about common programming assessments and their preferred software features for teaching programming. Forty K-12 teachers were invited to complete the survey. The responses were analyzed with thematic analysis using a bigram-based Latent Dirichlet Allocation topic modeling and descriptive statistics. Our study shows that the challenges include limited learning modules, an insufficient number of computers, limited programming skills, and limited computational thinking skills. Scratch was the most common programming language used and many programming assessments were about debugging a program or writing a small program. Visualization and animation can be helpful in teaching programming.

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Clustering Analysis of a Spatiotemporal Dataset with a Novel Kernel Density Estimator

Clustering Analysis of a Spatiotemporal Dataset with a Novel...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jen-Chien Yu Chun-Chieh Yang John Reuben Gilbert Rou-Jun Liu Yen-Jen Oyang Meng-Han Yang Department of Computer Science and Information Engineering National Kaohsiung University of Science and Technology Kaohsiung City Taiwan ROC Master Program in Statistics National Taiwan University Taipei Taiwan ROC Department of Computer Science & Information Engineering National Taiwan University Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University

A vast number of spatiotemporal datasets collected from a wide range of sources has motivated scientists to develop effective approaches to identify interesting patterns hidden in these datasets. In this respect, kernel density estimators, which belong to a class of non-parametric estimators in statistics, have been widely exploited in recent years. With this background, we have developed a novel kernel density estimator aiming to provide accurate analysis results. According to the evaluation with a real spatiotemporal dataset, which collected emergency medical service records in a county in the United States, the proposed kernel density estimator can approximate the probability density function significantly more accurately than a conventional kernel density estimator. Furthermore, we have exploited the proposed kernel density estimator to identify interesting patterns hidden in the real spatiotemporal dataset.

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