scholarly journals Computational Logistics for Container Terminal Handling Systems with Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Bin Li ◽  
Yuqing He
Keyword(s):  
Deep Learning ◽  
Container Terminal ◽  
Container Terminals ◽  
Hardware Platform ◽  
Logistics Network ◽  
Carbon Efficiency ◽  
The Past ◽  
Global Logistics ◽  
High Degree ◽  
Deep Learning Model

Container terminals are playing an increasingly important role in the global logistics network; however, the programming, planning, scheduling, and decision of the container terminal handling system (CTHS) all are provided with a high degree of nonlinearity, coupling, and complexity. Given that, a combination of computational logistics and deep learning, which is just about container terminal-oriented neural-physical fusion computation (CTO-NPFC), is proposed to discuss and explore the pattern recognition and regression analysis of CTHS. Because the liner berthing time (LBT) is the central index of terminal logistics service and carbon efficiency conditions and it is also the important foundation and guidance to task scheduling and resource allocation in CTHS, a deep learning model core computing architecture (DLM-CCA) for LBT prediction is presented to practice CTO-NPFC. Based on the quayside running data for the past five years at a typical container terminal in China, the deep neural networks model of the DLM-CCA is designed, implemented, executed, and evaluated with TensorFlow 2.3 and the specific feature extraction package of tsfresh. The DLM-CCA shows agile, efficient, flexible, and excellent forecasting performances for LBT with the low consuming costs on a common hardware platform. It interprets and demonstrates the feasibility and credibility of the philosophy, paradigm, architecture, and algorithm of CTO-NPFC preliminarily.

scholarly journals Truck Appointment System for Cooperation between the Transport Companies and the Terminal Operator at Container Terminals

2020 ◽  
Vol 11 (1) ◽  
pp. 168
Author(s):  
Hyeonu Im ◽  
Jiwon Yu ◽  
Chulung Lee
Keyword(s):  
Large Scale ◽  
Container Terminal ◽  
Container Terminals ◽  
The Past ◽  
Cooperation Model ◽  
Appointment System ◽  
New Form ◽  
Truck Appointment System ◽  
Import Container ◽  
Very High

Despite the number of sailings canceled in the past few months, as demand has increased, the utilization of ships has become very high, resulting in sudden peaks of activity at the import container terminals. Ship-to-ship operations and yard activity at the container terminals are at their peak and starting to affect land operations on truck arrivals and departures. In response, a Truck Appointment System (TAS) has been developed to mitigate truck congestion that occurs between the gate and the yard of the container terminal. The vehicle booking system is developed and operated in-house at large-scale container terminals, but efficiency is low due to frequent truck schedule changes by the transport companies (forwarders). In this paper, we propose a new form of TAS in which the transport companies and the terminal operator cooperate. Numerical experiments show that the efficiency of the cooperation model is better by comparing the case where the transport company (forwarder) and the terminal operator make their own decision and the case where they cooperate. The cooperation model shows higher efficiency as there are more competing transport companies (forwarders) and more segmented tasks a truck can reserve.

scholarly journals Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN

2022 ◽  
Vol 3 (4) ◽  
pp. 322-335
Author(s):  
C. R. Nagarathna ◽  
M. Kusuma
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Hybrid Model ◽  
Magnetic Resonance Images ◽  
Medical Field ◽  
The Past ◽  
Learning Techniques ◽  
Learning Technique ◽  
Deep Learning Model

Since the past decade, the deep learning techniques are widely used in research. The objective of various applications is achieved using these techniques. The deep learning technique in the medical field helps to find medicines and diagnosis of diseases. The Alzheimer’s is a physical brain disease, on which recently many research are experimented to develop an efficient model that diagnoses the early stages of Alzheimer’s disease. In this paper, a Hybrid model is proposed, which is a combination of VGG19 with additional layers, and a CNN deep learning model for detecting and classifying the different stages of Alzheimer’s and the performance is compared with the CNN model. The Magnetic Resonance Images are used to analyse both models received from the Kaggle dataset. The result shows that the Hybrid model works efficiently in detecting and classifying the different stages of Alzheimer’s.

scholarly journals One-Stage Logo Detection Framework using AdaBoost Resnet50 Backbone

2019 ◽  
Vol 8 (3) ◽  
pp. 451-457
Keyword(s):  
Deep Learning ◽  
Gradient Descent ◽  
Training Algorithm ◽  
The Past ◽  
One Stage ◽  
Input Dataset ◽  
Potential Applications ◽  
Or Organization ◽  
Deep Learning Model ◽  
Wide Potential

Logo is an important asset as it is designed to express identity or character of the company or organization that owns the logo. The advent of deep learning methods and proliferated of logo images sample dataset in the past decade has made automated logo detection from digital images or video an interesting computer vision problem with wide potential applications. This paper presents a novel one-stage logo detector framework in which the backbone of the proposed logo detector is a deep learning model which is trained supervisedly using gradient descent training algorithm and the target logo classes as input dataset. The experiment results showed that AdaBoost Resnet50 (0.58 MAP) as the logo detector backbone outperforms Resnet50 (0.56 MAP), VGG19 (0.32 MAP), and AdaBoost VGG19 (0.56 MAP).

scholarly journals Deep Learning System for COVID-19 Diagnosis Aid Using X-ray Pulmonary Images

2020 ◽  
Vol 10 (13) ◽  
pp. 4640 ◽  
Author(s):  
Javier Civit-Masot ◽  
Francisco Luna-Perejón ◽  
Manuel Domínguez Morales ◽  
Anton Civit
Keyword(s):  
Deep Learning ◽  
High Sensitivity ◽  
Roc Curves ◽  
Learning System ◽  
Invasive Technique ◽  
X Ray ◽  
Non Invasive ◽  
Specialist Doctor ◽  
High Degree ◽  
Deep Learning Model

The spread of the SARS-CoV-2 virus has made the COVID-19 disease a worldwide epidemic. The most common tests to identify COVID-19 are invasive, time consuming and limited in resources. Imaging is a non-invasive technique to identify if individuals have symptoms of disease in their lungs. However, the diagnosis by this method needs to be made by a specialist doctor, which limits the mass diagnosis of the population. Image processing tools to support diagnosis reduce the load by ruling out negative cases. Advanced artificial intelligence techniques such as Deep Learning have shown high effectiveness in identifying patterns such as those that can be found in diseased tissue. This study analyzes the effectiveness of a VGG16-based Deep Learning model for the identification of pneumonia and COVID-19 using torso radiographs. Results show a high sensitivity in the identification of COVID-19, around 100%, and with a high degree of specificity, which indicates that it can be used as a screening test. AUCs on ROC curves are greater than 0.9 for all classes considered.

scholarly journals Fast Prediction of Distances Between Synthetic Routes with Deep Learning

2021 ◽  
Author(s):  
Samuel Genheden ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Tree Edit Distance ◽  
Route Prediction ◽  
Synthesis Routes ◽  
Synthetic Routes ◽  
Fast Prediction ◽  
High Degree ◽  
Deep Learning Model ◽  
Degree Of Similarity

We expand our recent work on clustering of synthesis routes and train a deep learning model to predict the distances between arbitrary routes. The model is based on an long short-term memory (LSTM) representation of a synthesis route and is trained as a twin network to reproduce the tree edit distance (TED) between two routes. The ML approach is approximately two orders of magnitude faster than the TED approach and enables clustering many more routes from a retrosynthesis route prediction. The clusters have a high degree of similarity to the clusters given by the TED-based approach and are accordingly intuitive and explainable. We provide the developed model as open-source (https://github.com/MolecularAI/route-distances).

scholarly journals Fast Prediction of Distances Between Synthetic Routes with Deep Learning

2021 ◽  
Author(s):  
Samuel Genheden ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Tree Edit Distance ◽  
Route Prediction ◽  
Synthesis Routes ◽  
Synthetic Routes ◽  
Fast Prediction ◽  
High Degree ◽  
Deep Learning Model ◽  
Degree Of Similarity

We expand our recent work on clustering of synthesis routes and train a deep learning model to predict the distances between arbitrary routes. The model is based on an long short-term memory (LSTM) representation of a synthesis route and is trained as a twin network to reproduce the tree edit distance (TED) between two routes. The ML approach is approximately two orders of magnitude faster than the TED approach and enables clustering many more routes from a retrosynthesis route prediction. The clusters have a high degree of similarity to the clusters given by the TED-based approach and are accordingly intuitive and explainable. We provide the developed model as open-source (https://github.com/MolecularAI/route-distances).

scholarly journals Deep Learning based Brain Tumour Segmentation

2021 ◽  
Vol 19 ◽  
pp. 234-241
Author(s):  
Pattabiraman V. ◽  
Harshit Singh
Keyword(s):  
Artificial Intelligence ◽  
Image Segmentation ◽  
Deep Learning ◽  
Loss Function ◽  
Brain Mri ◽  
Semantic Segmentation ◽  
Tumour Segmentation ◽  
Segmentation Image ◽  
High Degree ◽  
Deep Learning Model

Artificial Intelligence has changed our outlook towards the whole world and it is regularly used to better understand all the data and information that surrounds us in our everyday lives. One such application of Artificial Intelligence in real world scenarios is extraction of data from various images and interpreting it in different ways. This includes applications like object detection, image segmentation, image restoration, etc. While every technique has its own area of application image segmentation has a variety of applications extending from complex medical field to regular pattern identification. The aim of this paper is to research about several FCNN based Semantic Segmentation techniques to develop a deep learning model that is able to segment tumours in brain MRI images to a high degree of precision and accuracy. The aim is to try several different architecture and experiment with several loss functions to improve the accuracy of our model and obtain the best model for our classification including newer loss function like dice loss function, hierarchical dice loss function cross entropy, etc.

scholarly journals Fast prediction of distances between synthetic routes with deep learning

2022 ◽  
Author(s):  
Samuel Genheden ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Tree Edit Distance ◽  
Route Prediction ◽  
Synthetic Routes ◽  
Long Short Term Memory ◽  
Fast Prediction ◽  
High Degree ◽  
Deep Learning Model ◽  
Degree Of Similarity

Abstract We expand the recent work on clustering of synthetic routes and train a deep learning model to predict the distances between arbitrary routes. The model is based on an long short-term memory (LSTM) representation of a synthetic route and is trained as a twin network to reproduce the tree edit distance (TED) between two routes. The ML approach is approximately two orders of magnitude faster than the TED approach and enables clustering many more routes from a retrosynthesis route prediction. The clusters have a high degree of similarity to the clusters given by the TED-based approach and are accordingly intuitive and explainable. We provide the developed model as open-source.

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