Loading and Routing Decisions with Assembly Plan Selection

1999 ◽  
pp. 87-105
Author(s):  
Tadeusz Sawik
Keyword(s):  
Plan Selection

scholarly journals Comprehensive Perspective for Lung Cancer Characterisation Based on AI Solutions Using CT Images

2020 ◽  
Vol 10 (1) ◽  
pp. 118
Author(s):  
Tania Pereira ◽  
Cláudia Freitas ◽  
José Luis Costa ◽  
Joana Morgado ◽  
Francisco Silva ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Targeted Therapies ◽  
Treatment Plan ◽  
Personalised Medicine ◽  
Growth Factor Receptor ◽  
Accurate Diagnosis ◽  
Cancer Development ◽  
Thoracic Computed Tomography ◽  
Plan Selection ◽  
Ct Image Analysis

Lung cancer is still the leading cause of cancer death in the world. For this reason, novel approaches for early and more accurate diagnosis are needed. Computer-aided decision (CAD) can be an interesting option for a noninvasive tumour characterisation based on thoracic computed tomography (CT) image analysis. Until now, radiomics have been focused on tumour features analysis, and have not considered the information on other lung structures that can have relevant features for tumour genotype classification, especially for epidermal growth factor receptor (EGFR), which is the mutation with the most successful targeted therapies. With this perspective paper, we aim to explore a comprehensive analysis of the need to combine the information from tumours with other lung structures for the next generation of CADs, which could create a high impact on targeted therapies and personalised medicine. The forthcoming artificial intelligence (AI)-based approaches for lung cancer assessment should be able to make a holistic analysis, capturing information from pathological processes involved in cancer development. The powerful and interpretable AI models allow us to identify novel biomarkers of cancer development, contributing to new insights about the pathological processes, and making a more accurate diagnosis to help in the treatment plan selection.

OC-0439: Quantifying the benefit of online adaptive radiotherapy for rectal cancer compared to plan selection

2020 ◽  
Vol 152 ◽  
pp. S242
Author(s):  
R. De Jong ◽  
J. Visser ◽  
K. Crama ◽  
N. Van Wieringen ◽  
J. Wiersma ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Adaptive Radiotherapy ◽  
Plan Selection

scholarly journals OC-0243: Optimization of plan selection strategies in online ART of bladder cancer based on deformation vector fields

2013 ◽  
Vol 106 ◽  
pp. S94
Author(s):  
A. Vestergaard ◽  
J.F. Kallehauge ◽  
J.B.B. Petersen ◽  
J. Søndergaard ◽  
M. Høyer ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Vector Fields ◽  
Selection Strategies ◽  
Plan Selection

scholarly journals Kemampuan Guru Melaksanakan Pembelajaran Bahasa Indonesia Berbasis Kurikulum 2013 di SMPN 1 Pujut, Nusa Tenggara Barat

2019 ◽  
Vol 16 (2) ◽  
pp. 207-218
Author(s):  
Aa Nunu Aste Lestari ◽  
Rusdiawan Rusdiawan ◽  
Sudirman Sudirman
Keyword(s):  
Learning Process ◽  
Teaching Method ◽  
Lesson Plan ◽  
Evaluation Process ◽  
Material Development ◽  
Teaching Preparation ◽  
Plan Selection ◽  
Collection Data ◽  
K 13 ◽  
Selection Of

In this study, we address two purposes: to see the appropriateness between the lesson plan and comprehensible aspects of the 2013 Curriculum components, and appropriateness between teaching preparation made by teachers and the K-13 contents.  This study employed qualitative approached applying observation and questionnaire to collect data.  Analysis was based upon Miles and Huberman (1994) theories on data collection, data reduction, data display, verification and cioncklusion drawing.  Results show that the suitability between completeness of the components lesson plan with Curriculum 2013 is very less appropriate and the percentage suitability between learning process with teacher’s lesson plan 54.34% indicates low appropriateness.  Completeness and components of lesson plan are low in seven aspects:  indicator formulation, learning objectives,  material development,  teaching method plan, selection of media and learning resources, plan of teaching stages,  and assessment and evaluation process.  In addition, the appropriateness of learning process and plan in the lesson plan is perceived in different way dependent in teacher role and context when teaching in the classroom.  

scholarly journals EP-1514: Soft tissue vs. bony anatomy registration in an adaptive plan selection protocol for bladder cancer

2015 ◽  
Vol 115 ◽  
pp. S825-S826
Author(s):  
K.L. Jakobsen ◽  
J.B.B. Petersen ◽  
L.P. Muren ◽  
M. Hoyer ◽  
H. Lindberg ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Soft Tissue ◽  
Plan Selection ◽  
Bony Anatomy ◽  
Selection Protocol

Traffic Responsive Signal Timing Plan Generation based on Neural Network

2011 ◽  
pp. 229-240
Author(s):  
Azzam-ul-Asar ◽  
M. Sadeeq Ullah ◽  
Mudasser F. Wyne ◽  
Jamal Ahmed ◽  
Riaz-ul-Hasnain
Keyword(s):  
Neural Network ◽  
Closed Loop ◽  
Learning Rule ◽  
Loop System ◽  
Closed Loop System ◽  
Highway Capacity ◽  
Highway Capacity Manual ◽  
Real Time Traffic ◽  
Plan Selection ◽  
Artificial Neural Network Ann

This paper proposes a neural network based traffic signal controller, which eliminates most of the problems associated with the Traffic Responsive Plan Selection (TRPS) mode of the closed loop system. Instead of storing timing plans for different traffic scenarios, which requires clustering and threshold calculations, the proposed approach uses an Artificial Neural Network (ANN) model that produces optimal plans based on optimized weights obtained through its learning phase. Clustering in a closed loop system is root of the problems and therefore has been eliminated in the proposed approach. The Particle Swarm Optimization (PSO) technique has been used both in the learning rule of ANN as well as generating training cases for ANN in terms of optimized timing plans, based on Highway Capacity Manual (HCM) delay for all traffic demands found in historical data. The ANN generates optimal plans online to address real time traffic demands and thus is more responsive to varying traffic conditions.

scholarly journals Plan Selection based on Query Clustering

2002 ◽  
pp. 179-190 ◽  
Author(s):  
Antara Ghosh ◽  
Jignashu Parikh ◽  
Vibhuti S. Sengar ◽  
Jayant R. Haritsa
Keyword(s):  
Plan Selection
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