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Estimation of Relative Position of Drone using Fixed Size QR Code

Estimation of Relative Position of Drone using Fixed Size QR...

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2022 IEEE International Conference on systems, Man, and Cybernetics, SMC 2022

作者： Kang, Tae-Won Choi, Yong-Sik Jung, Jin-Woo Dongguk University Dept. of Artificial Intelligence Seoul Korea Republic of Dongguk University Dept. of Computer Science and Engineering Seoul Korea Republic of

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Inventory management is very important in any industry. In the past, people were directly involved in inventory management. However, this method is time consuming and requires a lot of labor. Therefore, in recent years, inventory management methods using drones are increasing. In this paper, we introduce an inventory management system using RFID and QR code, and a method of estimating the relative position of a drone based on the QR code, which is essential in the system. As a result of the experiment, when the center point of the QR code and the center point of the camera coincide, the error rate was very small, 0-1%. When the center points do not coincide, an error rate of up to 4% occurred. © 2022 IEEE.

关键词： Drones

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Identification of Fraudulent Transactions for Enhanced Credit Card Fraud Detection 4

Identification of Fraudulent Transactions for Enhanced Credi...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Gupta, Monica Gayathri, V. Narula, Rachna Kumar, Vijay Kaur, Surinder Singh, Neetu Bharati Vidyapeeth's College of Engineering Dept. of Electronics and Communication Engineering New Delhi India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering New Delhi India Bharati Vidyapeeth's College of Engineering Department of Information and Technology New Delhi India

ISBN: (纸本)9798350387490

With the flourishment of the digital era and the evolution of new techniques and technology, this paper highlights the ever-growing risk of credit card fraud. The advancing technology has provided fraudsters with new and untraceable techniques for scamming genuine users. With the increasing impact of fraud, financial institutions must use machine learning tools for scam detection to provide safety and security to their users' accounts. This paper consists of an interdisciplinary comparative analysis of ML algorithms: Random Forest Classifier, XGBoost, ADABoost, Logistic Regression, Decision Trees, and Artificial Neural Networks. The diverse selection of algorithms aims to determine the most effective algorithm with the highest accuracy score for predicting legitimate and fraudulent transactions. The methodology involves fetching data from an Excel file, training the models, testing them on 20% of the dataset, and finally obtaining results in a Word document, having a confusion matrix and accuracy of each algorithm. By comparing the results of the confusion matrix and accuracy score, the Random Forest Classifier and XGBoost demonstrate the highest accuracy scores, while Logistic Regression yields a lower accuracy score. © 2024 IEEE.

关键词： Logistic regression

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Grep: Performance Enhancement in MultiCore Processors using an Adaptive Graph Prefetcher

Grep: Performance Enhancement in MultiCore Processors using ...

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IEEE computer Society Annual Symposium on VLSI

作者： Indranee Kashyap Dipika Deb Nityananda Sarma School of Computer Science and Engineering RV University India Dept. of Computer Science and Engineering Tezpur University India Dept. of Computer Science and Engineering Indian Institute of Technology Guwahati India

Memory latency and off-chip bandwidth have been struggling to keep up with computing performance in modern computer systems. In this regard, prefetching helps in masking the long memory access latency at various cache levels by continuously monitoring an application’s memory access pattern. Upon detecting a pattern, it prefetches cache block ahead of its use. However, complex patterns such as directed or indirected pointer access, linked lists, and so on does not adhere to any specific pattern and hence, makes prefetching impossible. The paper proposes Grep, an adaptive graph based data prefetcher that monitors L1D cache misses and prefetches block in L2 cache. Unlike state-of-the-art prefetchers, Grep does not search for patterns in the miss stream. Rather, it generates a predecessor-successor relationship among the cache misses by constructing an occurrence graph that stores the frequency and sequence of subsequent cache block accesses. Therefore, both regular and irregular patterns in the miss stream can be predicted. Upon an address match in the occurrence graph, Grep prefetches block with a confidence value. Experimentally, it improves prefetch coverage and accuracy by 35.5% and 18.8%, respectively, compared to SPP.

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A Semi-Supervised Approach For Detecting Agricultural Land Reduction In Bangladesh Using Satellite Images

A Semi-Supervised Approach For Detecting Agricultural Land R...

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Computing, Applications and systems (COMPAS), IEEE International Conference on

作者： Wahid Mahmood Shafin Sk. Md. Masudul Ahsan Dept. of Computer Science and Engineering Khulna University of Engineering & Technology (KUET) Khulna Bangladesh

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

Recently, remote sensing change detection has significantly improved because of greater access to global satellite images and developments in satellite technology. Several CNN-based techniques have demonstrated exceptional precision. Detecting the amount of agricultural area that has been reduced is a type of change detection problem that can easily be solved using deep learning methods on satellite data. But the majority of deep learning methods have been specifically created for high-resolution images. This data lacks global coverage, consists of at most 3 to 4 bands, and is costly. The Sentinel-2 satellite, which was launched in 2015, offers medium-resolution multi-spectral image data consisting of 14 bands and provides free global coverage. Having more bands enhances the capabilities for model analysis. But there is a lack of labeled satellite data. This research proposes a semi-supervised method for generating ground truth by employing a multi-step procedure. This technique began with semi-supervised classification to label the images, followed by a sequence of refinement processes to enhance the accuracy of the ground truth mask. The mask was created using the XGBoost machine learning technique. The dataset was assessed using the output images generated by the trained model. Detecting changes in low-resolution image may be difficult, but proposed method will greatly streamline the procedure. Applying this approach revealed an approximately 8.35% decrease in agricultural land area inside the test image area between 2018 and 2022. In the test area of 237.77 square kilometers, the reduced area measures approximately 19.861 square kilometers.

关键词： Deep learning Deforestation Satellites Accuracy Computational modeling Urban areas Area measurement Streaming media Satellite images Remote sensing

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Hybrid Deep Learning Approach for Nonfunctional Software Requirements Classifications 3

Hybrid Deep Learning Approach for Nonfunctional Software Req...

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3rd International Conference on Communication, Computing and Digital systems, C-CODE 2023

作者： Rahman, Kiramat Ghani, Anwar Ahmad, Rashid Sajjad, Syed Haseeb International Islamic University Dept. of Software Engineering Islamabad Pakistan Big Data Research Center Jeju National University Jeju Korea Republic of Inernational Islamic University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350332391

This paper explores the concept of algorithmic hybridization, which involves combining various machine learning (ML) algorithms to enhance performance by utilizing the benefits of both simultaneously. This study presents a framework that utilizes a combination of long short-term memory (LSTM) and bidirectional LSTM (BiLSTM) with artificial neural networks (ANN) to classify non-functional requirements (NFR). The task of NFR classification is challenging due to the scarcity of supervised learning data. The effectiveness of the proposed approach was assessed by comparing the performance of our integrated model with that of single LSTM and BiLSTM models. To conduct this evaluation, we combined two datasets consisting of 1000 non-functional requirements (NFR). The experimental findings revealed that the proposed approach is effective as the hybrid models exhibited better precision, recall, and F-1 score compared to its counterparts. © 2023 IEEE.

关键词： Requirements engineering

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Highly Accurate Two Channel Single-Cycle EOG Classification for Smart Wearable Technologies

Highly Accurate Two Channel Single-Cycle EOG Classification ...

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IEEE International Conference on Biomedical engineering, computer and Information Technology for Health

作者： Wakim Sajjad Sakib Abdullah Bin Shams Raisa Tasrin Ridi Md. Mohsin Sarker Raihan Raihani Jannat Shapnil Dept. of Electronics & Communication Engineering Institute Of Science and Technology Dhaka Bangladesh Dept. of Electrical & Computer Engineering University of Toronto Toronto Ontario Canada Dept. of Computer Science & Engineering American International University - Bangladesh Dhaka Bangladesh Dept. of Electrical & Electronic Engineering University of Science and Technology Chittagong Chattogram Bangladesh

ISBN: (数字)9798331534356

ISBN: (纸本)9798331534363

Motion of the eye generates an electrical potential difference between the cornea and retina. This signal, known as the Electrooculogram (EOG), is unique and characteristic of eye movements. This can be leveraged for noninvasive eye-tracking. Although the accuracy of this procedure decreases as the number of ocular motions increases, it is crucial for enabling more complex tasks in the Human-computer Interaction (HCI) field. The performance further deteriorates when the motion classification is constrained within a single EOG cycle. This is imperative and significant for real-time practical applications. In this experimental study, (i) we have addressed the complexities in working with real-time single cycle EOG signals and provided necessary solutions (ii) worked with nine distinct eye movements, and (iii) proposed a cascaded Artificial Neural Network (ANN) approach to classify nine different eye movements based on single-cycle EOG signal data. Leveraging statistical properties and dominant frequency analysis of the EOG signals, our approach achieved an accuracy of 99.26%, precision of 99.26%, recall of 99.32%, and a F1-score of 99.25%. This is a significant improvement over past studies conducted by various researchers for similar purposes and to the knowledge of the authors, such high-performance metrics over this wide range of eye movements based on single-cycle EOG signals have not been previously reported.

关键词： Measurement Electrooculography Accuracy Low-pass filters Artificial neural networks computer architecture Real-time systems Complexity theory Wearable devices Robots

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A Digital Healthcare Service Architecture for Seniors Safety Monitoring in Metaverse

A Digital Healthcare Service Architecture for Seniors Safety...

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2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023

作者： Qu, Qian Xu, Ronghua Sun, Han Chen, Yu Sarkar, Sumantra Ray, Indrajit Binghamton University Dept. of Electrical and Computer Engineering BinghamtonNY13902 United States Binghamton University School of Management BinghamtonNY13902 United States Colorado State University Dept. of Computer Science Fort CollinsCO80523 United States

ISBN: (纸本)9798350333336

We have been witnessing an unprecedented increase in the aging population in human history. It is nontrivial to ensure the health and safety of seniors living alone. The prohibitive human labor cost necessitates more sustainable, technology-oriented approaches instead of labor-intensive solutions. The raising digital healthcare services (DHS) leveraging the Internet of Medical Things (IoMT), Digital Twins (DT), and advanced fifth-generation and beyond (B5G) wireless communication technology, are widely recognized as promising solutions. By enabling a seamless interwoven of the physical world and cyberspace, Metaverse makes an ideal home for the next generation of DHS. Thanks to characteristics of decentralization, traceability, and unalterability, Blockchain is envisioned to enhance security properties in Metaverse. This paper proposes MetaSafe, a DHS architecture for seniors' safety monitoring in Metaverse. Based on monitoring data collected by sensors, the activities and status of seniors, who are considered as the physical objects (PO), are mirrored to corresponding logical objects (LO) in a virtual community in the Metaverse, where activity recognition, potential risk prediction, and alert generation are realized. By leveraging Non-Fungible Token (NFT) technology to tokenize identities (POs and LOs) and data streams of the DHS on the blockchain, an NFT-based authentication fabric allows for verifiable ownership and traceable transferability during the data-sharing process. Specifically, an instant alerting system is introduced in this work that leverages a hybrid algorithm combining the singular spectrum analysis (SSA) approach with the long-short-term memory (LSTM) networks. Through an extensive experimental study, MetaSafe is validated as a feasible and promising approach to protect seniors living alone. © 2023 IEEE.

关键词： Blockchain

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Using Artificial Intelligence for Water Pipeline Infrastructure Asset Management

Using Artificial Intelligence for Water Pipeline Infrastruct...

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Pipelines 2022 Conference

作者： Vekaria, Darshan Vishwakarma, Anmol Sinha, Sunil Dept. of Computer Science Virginia Tech. United States Dept. of Civil and Environmental Engineering Virginia Tech. United States

ISBN: (数字)9780784484302

ISBN: (纸本)9780784484302

It is critical for society that we transform our siloed water management and infrastructure systems into smart, connected, sustainable, and resilient systems. This transformation can help us to address the effects of increasing extreme climate events, ecosystem demands, rapid global urbanization, and infrastructure deterioration from age and neglect. As water utilities improve their asset management programs, it is imperative that the data-driven decision support systems represent the complexities within water pipeline infrastructure systems. Artificial intelligence (AI) techniques can enable modelers to train mathematical algorithms to learn complex patterns from data and represent the water pipeline infrastructure systems accurately. PIPEiD is a national database platform that uses artificial intelligence (AI) and machine learning techniques to assess the performance and risk of water pipelines to help utilities better assess pipe replacement decisions and allocate funding. PIPEiD (Pipeline Infrastructure Database) will assist water sector utilities to manage water pipeline infrastructure systems more effectively for performance, resiliency, and sustainability. PIPEiD will provide the secure, robust, and centralized web-based database platform to address all three major infrastructure asset management levels: strategic, tactical, and operational for utilities of all sizes (small, medium, and large) across the country. The research team collected field performance data for potable, raw, and reuse water pipelines made from materials reflecting the wide range of pipes currently in the ground throughout the US, including cast and ductile iron, prestressed concrete cylinder pipe, reinforced concrete, steel, thermoplastic, PVC, and asbestos. The researchers worked to collect data distributed across different ecological areas, or cohorts, organized based on the climatic conditions of the 500 water utilities' locations. These cohorts included coastal, arid, Arctic, and mo

关键词： Potable water

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Implementation of human-following robot system applying virtual delay to posture of a leader

Implementation of human-following robot system applying virt...

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Image Processing and Robotics (ICIP), International Conference on

作者： Fumiaki Takemori Dept. of Electrical Engineering and Computer Science Faculty of Engineering Tottori University Tottori JAPAN

ISBN: (数字)9798350374766

ISBN: (纸本)9798350374773

This paper develops an implementation of a measurement and control system in which a vehicle follows its predecessor while maintaining a certain distance. First, we construct a model that virtually delays the reference coordinates of the leading target backwards, and propose a control law that follows the steering angle of the following vehicle to that virtual delay point. Next, a range sensor is used to detect the position and orientation of the leader, and the position and orientation is estimated using a template matching method based on the person’s body contour point group measured by the sensor. Finally, the effectiveness (including robustness) of the proposed system is demonstrated in some simulations and human tracking experiments.

关键词： Target tracking Robot kinematics Image processing Position measurement Robot sensing systems Control systems Turning

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Development of QR Code-Guided Autonomous Navigation system for Grape Cultivation Robot in Overhead Trellis Vineyard

Development of QR Code-Guided Autonomous Navigation System f...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Ren Fujimoto Chee Siang Leow Hideaki Yajima Koji Makino Xiaoyang Mao Hiromitsu Nishiizaki Dept. of Mechatoronics University of Yamanashi Kofu Japan Dept. of Computer Science and Engineering University of Yamanashi Kofu Japan

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

This paper presents a novel autonomous navigation system for grapevine cultivation robots operating in overhead trellis vineyards, a challenging environment characterized by irregular grape cluster distribution and complex 3D structures. The system integrates two complementary navigation approaches: high-precision positioning under target grape bunches using YOLOv8-based object detection of QR codes, and large-scale vineyard navigation using global navigation satellite system (GNSS). Each QR code uniquely identifies a grape bunch and provides access to its associated cultivation data. Experimental evaluations in a simulated vineyard environment demonstrated an 83.3% success rate for object detection-based positioning beneath grape bunches and 90% accuracy for GNSS-based navigation within a 1.0 m radius of the target locations. These results validate the system's effectiveness in automating navigation tasks within overhead trellis vineyards, offering a promising solution to address labor shortages and improve the efficiency of grape cultivation practices.

关键词： Precision agriculture Global navigation satellite system Three-dimensional displays Accuracy Service robots Pipelines QR codes Object detection Autonomous robots

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