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Topic Review
3D Estimation Using an Omni-Camera and a Spherical-Mirror
There is a novel approach for estimating the 3D information of an observed scene utilizing a monocular image based on a catadioptric imaging system employing an omnidirectional camera and a spherical mirror. Researchers aim to develop a method that is independent of learning and makes it possible to capture a wide range of 3D information using a compact device.
  • 295
  • 08 Aug 2023
Topic Review
Blockchain-Based Traffic Bottleneck Management System
To alleviate traffic congestion, it is necessary to effectively manage traffic bottlenecks. In existing research, travel demand prediction for traffic bottlenecks is based on travel behavior assump￾tions, and prediction accuracy is low in practice. Thus, the effect of traffic bottleneck management strategies cannot be guaranteed. Management strategies are often mandatory, leading to problems such as unfairness and low social acceptance. To address such issues, this paper proposes managing traffic bottlenecks based on shared travel plans. To solve the information security and privacy prob￾lems caused by travel plan sharing and achieve information transparency, travel plans are shared and regulated by blockchain technology. To optimize the operation level of traffic bottlenecks, travel plan regulation models under scenarios where all/some travelers share travel plans are proposed and formulated as linear programming models, and these models are integrated into the blockchain with smart contract technology. Furthermore, travel plan regulation models are tested and verified using traffic flow data from the Su-Tong Yangtze River Highway Bridge, China. The results indicate that the proposed travel plan regulation models are effective for alleviating traffic congestion. The vehicle transfer rate and total delay rate increase as the degree of total demand increases; the vehicle transfer rate increases as the length of the time interval decreases; and the vehicle transfer rate and total delay rate increase as the number of vehicles not sharing their travel plans increases. By using the model and method proposed in this paper, the sustainability of urban economy, society, and environment can be promoted.
  • 50
  • 27 Mar 2024
Topic Review
Budget Allocation with Combinatorial Constraints
Budget allocation problems, commonly referred to as capital budgeting problems, often involve many constraints. Consequently, efficiently and effectively solving these problems becomes increasingly challenging. Advancements in linear programming-based row generation and optimization-based sorting methods offer promising solutions to address these challenges.
  • 109
  • 21 Dec 2023
Topic Review
CCR Model (DEA)
The first Data Envelopment Analysis (DEA) model developed by Charnes, Cooper and Rhodes (1978) under the assumption of a Constant Returns to Scale production technology, i.e.,  when an increase in the production resources results in a proportional increase in the output.
  • 13.5K
  • 30 May 2021
Topic Review
Chaotic Intermittency
Chaotic intermittency is characterized by a signal that alternates aleatory between long regular (pseudo-laminar) phases and irregular bursts (pseudo-turbulent or chaotic phases).
  • 290
  • 16 Jun 2023
Topic Review
Chatbot-Based Natural Language Interfaces for Data Visualisation
Reality (AR) or Virtual Reality (VR), particularly for advanced visualisations, expanding guidance strategies beyond current limitations, adopting intelligent visual mapping techniques, and incorporating more sophisticated interaction methods. 
  • 406
  • 28 Jun 2023
Topic Review
Complexity of Needs Model (DEA)
Data Envelopment Analysis (DEA) is a powerful non-parametric engineering tool for estimating technical efficiency and the production capacity of service units. The Complex-of-Needs Allocation Model proposed by Nepomuceno et al. (2020) is a two-step methodology for prioritizing hospital bed vacancy and reallocation during the COVID-19 pandemic. The framework determines the production capacity of hospitals through Data Envelopment Analysis and incorporates the Complexity of Needs in two categories for the reallocation of beds throughout the medical specialties. As a result, we have a set of inefficient health-care units presenting less complex bed slacks to be reduced, i.e. to be allocated for patients presenting more severe conditions.
  • 680
  • 08 Apr 2021
Topic Review
Conditional Frontier Analysis (DEA)
Conditional Frontier Analysis is part of the Nonparametric Robust Estimators proposed to overcome some drawbacks in the traditional Data Envelopment Analysis (DEA) and Free Disposal Hull (FDH) measures for the technical efficiency. In special, this methodology extends the nonparametric input/output production technology to robustly account for extreme values or outliers in the data, and allow measuring the effect of external environmental variables on the efficiency of Decision Making Units (DMUs). 
  • 864
  • 01 May 2021
Topic Review
Convolutional Neural Network
Convolutional neural network (CNN)-based deep learning (DL) has a wide variety of applications in the geospatial and remote sensing (RS) sciences, and consequently has been a focus of many recent studies. 
  • 803
  • 29 Jan 2022
Topic Review
COVID-19 Pandemic Prediction
Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to the lack of essential data and uncertainty, the epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19, and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are proposed to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for 9 days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.
  • 734
  • 02 Feb 2021
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