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An iterative selection algorithm: A decision aid to select the best extra virgin olive oils competing in an international contest

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FOOD CONTROL 2023年第1期151卷

作者： Girard, Fabien Artaud, Jacques Pinatel, Christian Claeys-Bruno, Magalie Rebufa, Catherine Avignon Univ Aix Marseille Univ CNRS IRDIMBE Marseille France Maison Agr CTO F-13100 Aix En Provence France

Extra virgin olive oils (EVOO) are typically categorized in accordance with the nature of their fruitiness (green or ripe) and the intensity of their fruitiness (delicate, medium, or robust) in accordance with the proposal of the competitor (producers) or a chemical analysis certificate established by an official panel. Depending on the category in which EVOO participates, its categorization may either disadvantage or benefit its rating. To eliminate this ambiguity and maximize an EVOO's probability of winning, the jury of the International "Word Edible Oils" competition use an innovative method to assess the EVOOs using a restricted amount of sensory descriptors (aromatic maturity, structure and fruitiness). Independent of category, the best EVOOs with comparable organoleptic properties have been identified using a statistical processing of the scores of the three descriptors. This is an iterative version of the technique developed by Wootton, Sergent, and Phan-Tan-Luu (iWSP), which generates subspaces that enable a local selection of the best EVOOs in a 2D aromatic maturity vs structure plan. In each subspace, their ranking is determined by their fruitiness score. An iterative approach that takes into consideration the changing sequence of subspace formation and the varying size of the subspace enables the selection of the best EVOOs. The development of the iWSP algorithm enabled the elimination of category constraints, (ii) the selection of the best EVOOs among those with comparable organoleptic features based on a large number of simulations, and (iii) the creation of a ranking to aid the jury's final judgment.

关键词： selection algorithm Extra virgin olive oil Descriptors International competition

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Uninformative Biological Variability Elimination in Apple Soluble Solids Content Inspection by Using Fourier Transform Near-Infrared Spectroscopy Combined with Multivariate Analysis and Wavelength selection algorithm

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JOURNAL OF ANALYTICAL METHODS IN CHEMISTRY 2017年第1期2017卷 2525147-2525147页

作者： Zhang, Lin Zhang, Baohua Zhou, Jun Gu, Baoxing Tian, Guangzhao Nanjing Agr Univ Coll Engn Nanjing 210031 Jiangsu Peoples R China

Uninformative biological variability elimination methods were studied in the near-infrared calibration model for predicting the soluble solids content of apples. Four different preprocessing methods, namely, Savitzky-Golay smoothing, multiplicative scatter correction, standard normal variate, and mean normalization, as well as their combinations were conducted on raw Fourier transformnear-infrared spectra to eliminate the uninformative biological variability. Subsequently, robust calibration models were established by using partial least squares regression analysis and wavelength selection algorithms. Results indicated that the partial least squares calibration models with characteristic variables selected by CARS method coupled with preprocessing of Savitzky-Golay smoothing and multiplicative scatter correction had a considerable potential for predicting apple soluble solids content regardless of the biological variability.

关键词： multivariate analysis Biological Partial least squares Fourier Crab Apple near infrared reflectance spectroscopy selection algorithm total soluble solids Near infrared spectroscopy Pretreatment wavelengths

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Chaotic Clonal selection algorithm for Function Optimization

Chaotic Clonal Selection Algorithm for Function Optimization

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2010 International Conference on E-Health Networking,Digital Ecosystems and Techonlogies(2010电子健康网络、数字生态系统和技术国际会议 EDT 2010)

作者： Hongwei Dai Yu Yang Cunhua Li School of Computer and Engineering Huaihai Institute of Technology Lianyungang Jiangsu 222005 China

ISBN: (纸本)9781424455140;9781424455157

The chaotic initialization and chaotic search are introduced into Clonal selection algorithm (CSA) to overcome random antibody initialization and premature convergence problems in traditional CSA. Taking full advantages of the ergodic and stochastic properties of chaotic variables, antibodies with different affinity perform chaotic search to exploit local solution space. Experimental results on test functions demonstrate that the chaotic CSA outperforms the classical clonal selection algorithm.

关键词： Clonal selection algorithm Chaotic initialization Chaotic search Optimization Clonal selection selection algorithm initialization Solution space

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Hardware Implementation of Proposed Antenna selection algorithm and its Performance Evaluation Using Received Signals in Field Experiment

Hardware Implementation of Proposed Antenna Selection Algori...

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IEEE Vehicular Technology Conference

作者： Kazuhiko Mitsuyama Tetsuomi Ikeda Tomoaki Ohtsuki Japan Broadcasting Corporation (NHK) Science and Technology Research Laboratories Department of Information and Computer Science Keio University

ISBN: (纸本)9781467318808

Multiple-input multiple-output (MEMO) systems with antenna selection are practical ones that can intuitively alleviate the computational complexity at the receiver and achieve good reception performance. Channel correlation, not just carrier-to-noise ratio (CNR), has a great impact on the reception performance in MEMO channels. We propose a simple antenna selection algorithm that exploits the condition number of the channel matrix and a predetermined threshold CNR. This paper describes the hardware implementation of the proposed algorithm and its performance evaluation, which was conducted in an indoor measurement using received signals obtained in the actual mobile outdoor experiment. The results confirm that our proposed method provides good bit error rate performance by setting a threshold CNR properly.

关键词： Performance Evaluation selection algorithm received signal carrier to noise ratios bit error rate performance field experimentation channel matrix ANTENNA antennae condition number Computer hardware reception

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Landmarks selection algorithm for Wireless Sensor Networks

Landmarks Selection Algorithm for Wireless Sensor Networks

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IEEE International Conference on Self-Adaptive and Self-Organizing Systems

作者： Sergey Baskakov Information Systems and Telecommunications Department Bauman Moscow State Technical University Moscow Russian Federation

ISBN: (纸本)9780769534046

In this paper, we propose a distributed, self-organized landmarks selection algorithm that ensures different patterns of landmarks spread throughout deployment area of a wireless sensor network. The algorithm is highly scalable through decentralized implementation with low time and memory complexity. The proposed technique represents an optimal complexity algorithm for virtual coordinates routing protocols in large-scale wireless sensor networks, and our simulations show that it improves significantly virtual coordinates routing protocols performance, preserving the simplicity and high scalability of this routing method.

关键词： Wireless sensor networks selection algorithm landmarks Routing protocols Scalability Coordinates Distribute

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A Clonal selection algorithm with Levenshtein Distance based Image Similarity in Multidimensional Subjective Tourist Information and Discovery of Cryptic Spots by Interactive GHSOM

A Clonal Selection Algorithm with Levenshtein Distance based...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Takumi Ichimura Shin Kamada Faculty of Management and Information Systems Prefectural University of Hiroshima 1-1-71 Ujina-Higashi Minami-ku Hiroshima 734-8559 Japan Graduate School of Comprehensive Scientific Research Prefectural University of Hiroshima 1-1-71 Ujina-Higashi Minami-ku Hiroshima 734-8559 Japan

ISBN: (纸本)9781479906505

Mobile Phone based Participatory Sensing (MPPS) system involves a community of users sending personal information and participating in autonomous sensing through their mobile phones. Sensed data can also be obtained from external sensing devices that can communicate wirelessly to the phone. Our developed tourist subjective data collection system with Android smartphone can determine the filtering rules to provide the important information of sightseeing spot. The rules are automatically generated by Interactive Growing Hierarchical SOM. However, the filtering rules related to photograph were not generated, because the extraction of the specified characteristics from images cannot be realized. We propose the effective method of the Levenshtein distance to deduce the spatial proximity of image viewpoints and thus determine the specified pattern in which images should be processed. To verify the proposed method, some experiments to classify the subjective data with images are executed by Interactive GHSOM and Clonal selection algorithm with Immunological Memory Cells in this paper.

关键词： Levenshtein Distance Clonal selection algorithm Image Analysis Immunological Memory Cells Growing Hierarchical SOM Interactive GHSOM Smartphone based Participatory Sensing System Tourist Informatics Knowledge Discovery Levenshtein distance data mining image analysis images selection algorithm Tourist information services Clonal selection Interactive Device Sensor CFC1 gene DATA ACQUISITION SYSTEMS

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A Modified Fast Recursive Hidden Nodes selection algorithm for ELM

A Modified Fast Recursive Hidden Nodes Selection Algorithm f...

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International Joint Conference on Neural Networks

作者： Min Han Xinying Wang Faculty of Electronic Information and Electrical Engineering Dalian University of Technology

ISBN: (纸本)9781467314886

Extreme Learning Machine (ELM) is a new paradigm for using Single-hidden Layer Feedforward Networks (SLFNs) with a much simpler training method. The input weights and the bias of the hidden layer are randomly chosen and output weights are analytically determined. One of the open problems in ELM research is how to automatically determine network architectures for given tasks. In this paper, it is taken as a model selection problem, a modified fast recursive algorithm (MFRA) is introduced to quickly and efficiently estimate the contribution of each hidden layer node to the decrease of the net function, and then a leave one out (LOO) cross validation is used to select the optimal number of hidden layer nodes. Simulation results on both artificial and real world benchmark datasets indicate the effectiveness of the proposed method.

关键词： Extreme learning machine Model selection Time series Prediction Ecosystem models Elm NETWORK STRUCTURE selection algorithm Ganglia Recursive Fasting

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A Coherent Cluster Head selection algorithm Based on Pinnacle Weight

A Coherent Cluster Head Selection Algorithm Based on Pinnacl...

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International Conference on Computation, Automation and Knowledge Management (ICCAKM)

作者： Anju Gera Pawan Kumar Singh Deep Kiran Munjal Computer Science and Engineering G L. Bajaj Institute of Technology and Management Greater Noida

ISBN: (数字)9781728106663

ISBN: (纸本)9781728106670

In the last two decades, there has been a rapid growth in the use of the Internet and Communication MANET consists of number of nodes in which it communicates with each other. One of the main design issues in MANET is that they are power constrained. To spare the vitality, the hubs can be assembled as groups. Cluster heads are having more duties, for example, to limit the re-connection, topology changes, and the strength of the on request organizes. Clustering is system to limit the multifaceted nature by solving the issue and to make smaller groups called clusters. To achieve this efficient cluster head should be elected. In this paper “A coherent Cluster head selection algorithm based on pinnacle weight for MANET” is proposed from ECAM with additional parameter.

关键词： mobile ad hoc networks pattern clustering routing protocols telecommunication network topology Mobile ad hoc networks telecommunication network topology Routing protocols cluster head Cluster Analysis selection algorithm TACSTD1 protein, human

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Heuristic Relay Node selection algorithm for One-Hop Overlay Routing

Heuristic Relay Node Selection Algorithm for One-Hop Overlay...

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International Conference on Distributed Computing Systems Workshop

作者： Yin Chen Li Tang Jun Li Department of Automation Tsinghua University Beijing China Research Institute of Information Technology Tsinghua University Beijing China Tsinghua National Laboratory for Information Science and Technology Beijing China

ISBN: (纸本)9781424431755

This paper reviews the characteristics of overlay networks and defines effective relay nodes that can improve the performance of interactive real-time applications. A heuristic relay node selection algorithm for overlay routing is proposed. The algorithm can find effective relay nodes for end-to-end links in an overlay network, and is scalable and robust due to its decentralization and randomization. Based on the simulation results, the algorithm is able to maintain a good performance when 40% end nodes in an overlay network encounter failures.

关键词： Overlay networks Relay routing Relay node selecting overlay network Trunk Node selection algorithm Overlay decentralization end-to-end Heuristics Randomisation Network routing Scalability end node Ganglia

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A New Feature selection algorithm for Two-Class Classification Problems and Application to Endometrial Cancer

A New Feature Selection Algorithm for Two-Class Classificati...

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IEEE Annual Conference on Decision and Control

作者： M. Eren Ahsen Nitin K. Singh Todd Boren M. Vidyasagar Michael A. White Department of Bioengineering University of Texas at Dallas Department of Cell Biology UT Southwestern Medical Center

ISBN: (纸本)9781467320658

In this paper, we introduce a new algorithm for feature selection for two-class classification problems, called l_1-StaR. The algorithm consists of first extracting the statistically relevant features using the Student t-test, and then passing the reduced feature set to an l_1-norm support vector machine (SVM) with recursive feature elimination (RFE). The final number of features chosen by the l_1-StaR algorithm can be smaller than the number of samples, unlike with l_1-norm regression where the final number of features is bounded below by the number of samples. The algorithm is illustrated by applying it to the problem of determining which endometrial cancer patients are at risk of having the cancer spreading to their lymph nodes. The data consisted of 1,428 micro-RNAs measured on a data set of 94 patient samples (divided evenly between those with lymph node metastasis and those without). Using the algorithm, we identified a subset of just 15 micro-RNAs and a linear classifier based on these, that achieved two-fold cross validation accuracies in excess of 80%, and combined accuracy, sensitivity and specificity in excess of 93%.

关键词： Feature extraction endometrium cancer Linear classifier selection algorithm algorithms t-test Support Vector Network Metastatic Malignant Neoplasm to the Lymph Node cancer patient

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