Urban planning and development have undergone a complete transformation with the advent of the Internet of Things (IoT), and groundbreaking technologies have been made available for smart city efforts. The inclusion o...
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ISBN:
(数字)9798350364699
ISBN:
(纸本)9798350364705
Urban planning and development have undergone a complete transformation with the advent of the Internet of Things (IoT), and groundbreaking technologies have been made available for smart city efforts. The inclusion of Internet of Things (IoT) technology in the design and development of smart cities is examined in this article. The significance that these technologies play in improving resource management, modernizing urban infrastructure, and elevating people's quality of life is given special consideration. This paper evaluates existing Internet of Things (IoT) applications, like intelligent transportation systems, smart grids, and environmental monitoring, to show how the Internet of Things (IoT) might address common urban issues like traffic congestion, energy consumption, and environmental pollution. Furthermore, the essay examines case studies submitted by well-known smart cities to show how the Internet of Things (IoT) may be successfully used and how it affects the sustainability and efficiency of metropolitan areas. The results suggest that the Internet of Things not only facilitates real-time data collection and analysis but also fosters the creation of creative urban development ideas. Thanks to the research that was done, this study shows how the Internet of Things has the potential to impact future cities and make them more resilient, adaptable, and effective in meeting the needs of growing populations.
Bitcoin is a rapidly growing but extremely risky cryptocurrency. It marks a watershed moment in the history of cash. These days, digital currency is preferred to actual money. Bitcoin has decentralized authority and p...
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Free text reviews are abundantly distributed over the internet among the standard population of the world. Although online doctors were consulted during the pandemic, many drugs are taken without prescription by ordin...
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ISBN:
(数字)9781665474979
ISBN:
(纸本)9781665474986
Free text reviews are abundantly distributed over the internet among the standard population of the world. Although online doctors were consulted during the pandemic, many drugs are taken without prescription by ordinary people. Reviews are distributed about the medicines through online feeds, posts and blogs, which are merely helpful in identifying the side effects and their adverse reactions in combination with another drug or standalone adverse reactions. Spelling mistakes, jargon and short names are normalized throughout the medical terminologies using Natural language processing (NLP) and RxNorm from the reviews extracted from these sources. The concept of Bidirectional Encoder Representations from Transformers (BERT) is used to mine the association rules for the drug interactions when used in conjugation with another drug, and the idea of Adaptive Fuzzy logic neural networks is used to validate the results of association rules derived from the BERT. Inconsistencies identified by the regulations generated were rejected efficiently by the Support vector machines (SVM), Radial Basis Function (RBF)and the system shows 60.11 percentage of accuracy with 10 cross-fold validation, whereas the results are equivalent to the accuracy obtained using SVM with RBF kernel. The data set is reduced from its dimension using Linear discriminant analysis (LDA) by applying the mining process and BERT on two drugs, namely “Azithromycin” in conjugation with Buprenorphine has Type ‘A’ side effects, and Prozac in conjugation with Metronidazole shows different side effects. Deplin and Zoloft give very positive symptoms and are independent of dosage interactions.
computergraphics seeks to deliver compelling images, generated within a computing budget, targeted at a specific display device, and ultimately viewed by an individual user. The foveated nature of human vision offers...
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Several business applications and procedures are expected to be significantly disrupted by Blockchain-based technologies, which has significant consequences for ecommerce. In light of the potential blockchain based te...
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Several business applications and procedures are expected to be significantly disrupted by Blockchain-based technologies, which has significant consequences for ecommerce. In light of the potential blockchain based technology are popularly known as "trust building systems" that can be used in multiple business models and standard guidelines which evolved placed above a white period to achieve believe, serviceability, and enforcement in various domains such as cunsumers related business, business related connections have to be questioned and possibly adjusted. By enabling trustless trade interactions that work without specialized intermediaries or even central authority in the case of permission lessblock chains, Blockchain has the power to change how electronic commerce operates. Furthermore, giving universal access to immutable data across the whole supply chain may significantly alter the information and value exchange between businesses and consumers. The methodology presented in this paper are intended to motivate further study of the potential effects of Blockchain on e-commerce. technology, law, organizational and quality challenges, as well as consumer difficulties, are the four key categories. This paper demonstrates how Blockchain may affect many Electronic commercial components in these relevant fields.
We propose three novel solvers for estimating the relative pose of a multi-camera system from affine correspondences (ACs). A new constraint is derived interpreting the relationship of ACs and the generalized camera m...
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ISBN:
(纸本)9781665428132
We propose three novel solvers for estimating the relative pose of a multi-camera system from affine correspondences (ACs). A new constraint is derived interpreting the relationship of ACs and the generalized camera model. Using the constraint, we demonstrate efficient solvers for two types of motions assumed. Considering that the cameras undergo planar motion, we propose a minimal solution using a single AC and a solver with two ACs to overcome the degenerate case. Also, we propose a minimal solution using two ACs with known vertical direction, e.g., from an IMU. Since the proposed methods require significantly fewer correspondences than state-of-the-art algorithms, they can be efficiently used within RANSAC for outlier removal and initial motion estimation. The solvers are tested both on synthetic data and on real-world scenes from the KITTI odometry benchmark. It is shown that the accuracy of the estimated poses is superior to the state-of-the-art techniques.
Early fire detection and alarm are significantly important to reduce the losses caused by fire. Conventional methods in fire detection using smoke and heat detectors have disadvantages in accuracy, latency as well as ...
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ISBN:
(数字)9781728154718
ISBN:
(纸本)9781728154725
Early fire detection and alarm are significantly important to reduce the losses caused by fire. Conventional methods in fire detection using smoke and heat detectors have disadvantages in accuracy, latency as well as the detection area. In this paper, we propose and implement a real-time fire detection solution for large area surveillance using the unmanned aerial vehicle with an integrated visual detection and alarm system. The system includes a low-cost camera, a light weight companion computer, a flight controller as well as localization and telemetry modules. To achieve real-time detection, Single Shot MultiBox Detector (SSD) algorithm is implemented as the heart of the system. We used MobileNets base model, which more efficient for mobile and embedded vision applications, instead of conventional VGG-16/ResNet model to achieve the mean average precision of 92.7% with the detection speed of 26 FPS.
The paper provides a summary of the 2023 Unconstrained Ear Recognition Challenge (UERC), a benchmarking effort focused on ear recognition from images acquired in uncontrolled environments. The objective of the challen...
The paper provides a summary of the 2023 Unconstrained Ear Recognition Challenge (UERC), a benchmarking effort focused on ear recognition from images acquired in uncontrolled environments. The objective of the challenge was to evaluate the effectiveness of current ear recognition techniques on a challenging ear dataset while analyzing the techniques from two distinct aspects, i.e., verification performance and bias with respect to specific demographic factors, i.e., gender and ethnicity. Seven research groups participated in the challenge and submitted a seven distinct recognition approaches that ranged from descriptor-based methods and deep-learning models to ensemble techniques that relied on multiple data representations to maximize performance and minimize bias. A comprehensive investigation into the performance of the submitted models is presented, as well as an in-depth analysis of bias and associated performance differentials due to differences in gender and ethnicity. The results of the challenge suggest that a wide variety of models (e.g., transformers, convolutional neural networks, ensemble models) is capable of achieving competitive recognition results, but also that all of the models still exhibit considerable performance differentials with respect to both gender and ethnicity. To promote further development of unbiased and effective ear recognition models, the starter kit of UERC 2023 together with the baseline model, and training and test data is made available from: http://***/
This paper presents a summary of the Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data (SYN-MAD) held at the 2022 International Joint Conference on Biometrics (IJCB 2022). Th...
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Improving efficiency and patient satisfaction through better appointment scheduling is a problem for healthcare systems across the globe. To improve healthcare appointment scheduling procedures, this research proposes...
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ISBN:
(数字)9798331540661
ISBN:
(纸本)9798331540678
Improving efficiency and patient satisfaction through better appointment scheduling is a problem for healthcare systems across the globe. To improve healthcare appointment scheduling procedures, this research proposes a new method that makes use of cloud computing and reinforcement learning (RL) algorithms. to maximize healthcare provider efficiency while minimizing patient wait times, resource usage, and operational costs by dynamically learning and adapting scheduling rules using RL, taking use of the scalability and computing capacity of cloud infrastructure. To train RL agents, create a simulation environment that mimics real-world healthcare conditions. Our findings show that RL-based scheduling strategies are more efficient at scheduling appointments than conventional techniques, and we prove it via rigorous testing and assessment. Additionally, we demonstrate how our strategy can handle dynamic healthcare contexts with different patient numbers and resource restrictions, demonstrating its flexibility and resilience. The results show that RL approaches run by the cloud can change healthcare appointment scheduling for the better, leading to healthcare systems that are more responsive and flexible. Utilizing the complementary strengths of cloud computing and RL, our method provides a data-driven, scalable solution to the hard problem of healthcare schedule optimization, which improves both patient care and operational efficiency
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