scholarly journals Combination of optimization techniques to find of processing optimal condition of postharvest broccoli by vacuum cooling process

2018 ◽  
Vol 40 (1) ◽  
pp. 36222 ◽  
Author(s):  
Tian Ding ◽  
Charles Gobber ◽  
José Carlos Curvelo Santana ◽  
Wonder Alexandre Luz Alves ◽  
Sidnei Alves de Araújo ◽  
...  
Keyword(s):  
Weight Loss ◽  
Response Surface Methodology ◽  
Tabu Search ◽  
Optimal Condition ◽  
Response Surface ◽  
Search Algorithm ◽  
Optimization Techniques ◽  
Water Volume ◽  
Tabu Search Algorithm ◽  
Vacuum Cooling

This study aimed to investigate the impact of each factor on the weight loss of postharvest broccoli and treatment efficacy, and also attempted to fix the optimal condition for vacuum cooling treatment on postharvest broccoli by response surface methodology combined with tabu search techniques. Fresh broccoli samples were harvested from a Chinese farm and the green heads of selected samples were cut into smaller ones with approximately 3~4 cmdiameter, and sequentially equilibrated to room temperature. Pressure (200-600 Pa), broccoli weight (200-500 g), water volume (2-6 %, v v-1) and time (20-40 min) were used as factors and weight loss, final temperature and cost as responses. A tabu search algorithm was developed to find the optimum condition for processing broccoli and its initial condition were from response surface methodology. Results demonstrates a good adjust of tabu search algorithm in simulation of the broccoli freezing process. From tabu list the best condition were found as follows: the broccoli weight between 273.5 and 278.0 g with a water volume of 3.0%, processed for 40.0 min and at 200 Pa, where the weight loss was 0.34 ± 0.01%, of end temperature was 2.0 ± 0.0°C and profit percent was 99.66 ± 0.01%. 

scholarly journals A Response Surface Methodology (RSM) Approach for Optimizing the Attenuation of Human IgE-Reactivity to β-Lactoglobulin (β-Lg) by Hydrostatic High Pressure Processing

Foods ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 1741
Author(s):  
Xin Sun ◽  
Jialing Vivien Chua ◽  
Quynh Anh Le ◽  
Francisco Trujillo ◽  
Mi-Hwa Oh ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Optimal Condition ◽  
Response Surface ◽  
High Pressure Processing ◽  
Molecular Structures ◽  
Holding Time ◽  
P Value ◽  
Molecular Change ◽  
Ige Reactivity ◽  
Β Lactoglobulin

The response surface methodology (RSM) and central composite design (CCD) technique were used to optimize the three key process parameters (i.e., pressure, temperature and holding time) of the high-hydrostatic-pressure (HHP) processing either standalone or combined with moderate thermal processing to modulate molecular structures of β-lactoglobulin (β-Lg) and α-lactalbumin (α-La) with reduced human IgE-reactivity. The RSM model derived for HHP-induced molecular changes of β-Lg determined immunochemically showed that temperature (temp), pressure (p2) and the interaction between temperature and time (t) had statistically significant effects (p < 0.05). The optimal condition defined as minimum (β-Lg specific) IgG-binding derived from the model was 505 MPa at 56 °C with a holding time of 102 min (R2 of 0.81 and p-value of 0.01). The validation carried at the optimal condition and its surrounding region showed that the model to be underestimating the β-Lg structure modification. The molecular change of β-Lg was directly correlated with HHP-induced dimerization in this study, which followed a quadratic equation. The β-Lg dimers also resulted in the undetectable human IgE-binding.

scholarly journals Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm

2021 ◽  
Vol 11 (15) ◽  
pp. 6728
Author(s):  
Muhammad Asfand Hafeez ◽  
Muhammad Rashid ◽  
Hassan Tariq ◽  
Zain Ul Abideen ◽  
Saud S. Alotaibi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tabu Search ◽  
Decision Tree ◽  
Decision Trees ◽  
Search Algorithm ◽  
Learning Algorithms ◽  
Performance Comparison ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Tabu Search Algorithm

Classification and regression are the major applications of machine learning algorithms which are widely used to solve problems in numerous domains of engineering and computer science. Different classifiers based on the optimization of the decision tree have been proposed, however, it is still evolving over time. This paper presents a novel and robust classifier based on a decision tree and tabu search algorithms, respectively. In the aim of improving performance, our proposed algorithm constructs multiple decision trees while employing a tabu search algorithm to consistently monitor the leaf and decision nodes in the corresponding decision trees. Additionally, the used tabu search algorithm is responsible to balance the entropy of the corresponding decision trees. For training the model, we used the clinical data of COVID-19 patients to predict whether a patient is suffering. The experimental results were obtained using our proposed classifier based on the built-in sci-kit learn library in Python. The extensive analysis for the performance comparison was presented using Big O and statistical analysis for conventional supervised machine learning algorithms. Moreover, the performance comparison to optimized state-of-the-art classifiers is also presented. The achieved accuracy of 98%, the required execution time of 55.6 ms and the area under receiver operating characteristic (AUROC) for proposed method of 0.95 reveals that the proposed classifier algorithm is convenient for large datasets.

An extreme‐point tabu‐search algorithm for fixed‐charge network problems

Networks ◽  
2021 ◽  
Vol 77 (2) ◽  
pp. 322-340 ◽  
Author(s):  
Richard S. Barr ◽  
Fred Glover ◽  
Toby Huskinson ◽  
Gary Kochenberger
Keyword(s):  
Tabu Search ◽  
Extreme Point ◽  
Search Algorithm ◽  
Fixed Charge ◽  
Tabu Search Algorithm ◽  
Network Problems

scholarly journals A support vector machine with the tabu search algorithm for freeway incident detection

2014 ◽  
Vol 24 (2) ◽  
pp. 397-404 ◽  
Author(s):  
Baozhen Yao ◽  
Ping Hu ◽  
Mingheng Zhang ◽  
Maoqing Jin
Keyword(s):  
Support Vector Machine ◽  
Tabu Search ◽  
Traffic Management ◽  
Search Algorithm ◽  
Detection System ◽  
Support Vector ◽  
Incident Detection ◽  
Tabu Search Algorithm ◽  
Proposed Model ◽  
Parameter Values

Abstract Automated Incident Detection (AID) is an important part of Advanced Traffic Management and Information Systems (ATMISs). An automated incident detection system can effectively provide information on an incident, which can help initiate the required measure to reduce the influence of the incident. To accurately detect incidents in expressways, a Support Vector Machine (SVM) is used in this paper. Since the selection of optimal parameters for the SVM can improve prediction accuracy, the tabu search algorithm is employed to optimize the SVM parameters. The proposed model is evaluated with data for two freeways in China. The results show that the tabu search algorithm can effectively provide better parameter values for the SVM, and SVM models outperform Artificial Neural Networks (ANNs) in freeway incident detection.

A Tabu Search algorithm for the optimisation of telecommunication networks

1998 ◽  
Vol 106 (2-3) ◽  
pp. 357-372 ◽  
Author(s):  
Eugenio Costamagna ◽  
Alessandra Fanni ◽  
Giorgio Giacinto
Keyword(s):  
Tabu Search ◽  
Search Algorithm ◽  
Telecommunication Networks ◽  
Tabu Search Algorithm

Study on Tabu Search Algorithm for Multi-Cargo Loading Problem

2012 ◽  
Vol 178-181 ◽  
pp. 1802-1805
Author(s):  
Chun Yu Ren
Keyword(s):  
Tabu Search ◽  
Search Algorithm ◽  
Initial Solution ◽  
Neighborhood Search ◽  
Tabu Search Algorithm ◽  
Search Efficiency ◽  
Speed Of Convergence ◽  
Neighborhood Structure ◽  
Loading Problem

The paper is focused on the Multi-cargo Loading Problem (MCLP). Tabu search algorithm is an algorithm based on neighborhood search. According to the features of the problem, the essay centered the construct initial solution to construct neighborhood structure. For the operation, 1-move and 2-opt were applied, it can also fasten the speed of convergence, and boost the search efficiency. Finally, the good performance of this algorithm can be proved by experiment calculation and concrete examples.

Sign in / Sign up

Export Citation Format

Share Document