Advances in Processing, Mining, and Learning Complex Data: From Foundations to Real-World Applications

Processing, mining, and learning complex data refer to an advanced study area of data mining and knowledge discovery concerning the development and analysis of approaches for discovering patterns and learning models from data with a complex structure (e.g., multirelational data, XML data, text data, image data, time series, sequences, graphs, streaming data, and trees) [1–5]. These kinds of data are commonly encountered in many social, economic, scientific, and engineering applications. Complex data pose new challenges for current research in data mining and knowledge discovery as they require new methods for processing, mining, and learning them. Traditional data analysis methods often require the data to be represented as vectors [6]. However, many data objects in real-world applications, such as chemical compounds in biopharmacy, brain regions in brain health data, users in business networks, and time-series information in medical data, contain rich structure information (e.g., relationships between data and temporal structures). Such a simple feature-vector representation inherently loses the structure information of the objects. In reality, objects may have complicated characteristics, depending on how the objects are assessed and characterized. Meanwhile, the data may come from heterogeneous domains [7], such as traditional tabular-based data, sequential patterns, graphs, time-series information, and semistructured data. Novel data analytics methods are desired to discover meaningful knowledge in advanced applications from data objects with complex characteristics. This special issue contributes to the fundamental research in processing, mining, and learning complex data, focusing on the analysis of complex data sources

Grapes and Wine

Grape and Wine is a collective book composed of 18 chapters that address different issues related to the technological and biotechnological management of vineyards and winemaking. It focuses on recent advances, hot topics and recurrent problems in the wine industry and aims to be helpful for the wine sector. Topics covered include pest control, pesticide management, the use of innovative technologies and biotechnologies such as non-thermal processes, gene editing and use of non-Saccharomyces, the management of instabilities such as protein haze and off-flavors such as light struck or TCAs, the use of big data technologies, and many other key concepts that make this book a powerful reference in grape and wine production. The chapters have been written by experts from universities and research centers of 9 countries, thus representing knowledge, research and know-how of many regions worldwide

Comparison of different methodologies to estimate bunch compactness

Mestrado em Engenharia de Viticultura e Enologia (Double Degree) / Instituto Superior de Agronomia. Universidade de Lisboa / Faculdade de Ciências. Universidade do PortoBunch compactness (BC) is a key target for wine sector because it affects disease susceptibility, berry ripening among other grapes characteristics. The most common method to estimate BC is the O.I.V. descriptor n°204: manual and subjective. Objective and automated methods are based on indices, using different relations between bunch traits, some obtained manually and other automatically through image analysis (as example: BW – weight; BV – volume; ML – maximum length; A – projected area; MVO – morphological volume; V3 – derived volume; BN – berries number). All the variables were significantly and positively correlated between each other: the highest Pearson correlation coefficient was between BW and BV (r = 0.99) followed by BW and A (r = 0.95). Fourteen compactness indices (CI) were tested (9 published and 5 created) on 61 Syrah bunches. These indices were then correlated with the mode of the O.I.V. descriptor n°204, where 11 were positively correlated and three were negatively correlated (CI-3, CI-3a, CSF). The index CI-10a, which relates bunch weight and maximum length, was the most suitable one to define BC (r = 0.78). In the frame of the EU VINBOT project, to improve BW estimation finding the best explanatory variables, a stepwise regression analysis between BW and the variables considered easy to extract by automated image analysis (A1 – projected area, V3 – volume 3, BN – berries number and CI-10a as index) was performed. The variable which explained best BW was A1 (partial R2 = 0.905), followed by CI-10a and V3 with a much smaller contribution (partial R2 <0.06 and partial R2<0.007, respectively). The variable BN was not selected by the model. We concluded that BC can be estimated in an objective and automatic way using image analysis. Furthermore, such estimations can enhance BW prediction by using BC as one of the explanatory variables which can improve automatic yield estimation methodologiesN/

Feature Extraction and Classification of the Forewings of Three Moth Species based on Digital Images

The main objective of this research was to find out the possibility to use digital images of forewings in the species identification of codling moth. The suitability of different areas of forewing as identification marks was also determined. Digital RGB images were used to determine the features of forewings of three different Cydia species (Lepidoptera, Tortricidea). The chosen species were Cydia pomonella, Cydia splendana and Cydia strobilella. Text-based descriptions of the visual appearances of the moth species were used in feature selection. Image processing methods were applied on 6 different areas of 12 different forewings. 168 local features were calculated for each area. Features included direct pixel-wise intensity values and spatially filtered values. Stepwise regression was performed in order to reduce the number of features in linear models. The models were tested with linear regression analysis and hierarchical agglomerative clustering. Based on this research, Cydia pomonella can be identified from the two other Cydia species by forewing images. The identification was more reliable when the features of all 6 target areas were included compared to the case that the features of only 3 target areas were included. However, the forewings of Cydia pomonella were separated correctly from the forewings of two other Cydia species with 3 visible areas. Identification of sitting Cydia pomonella can be based on the measured or calculated features in the white-brown veined area in the middle of the forewing and in the bronze coloured oval in the sub marginal area but possibly not in the dark brown stripe in the inner margin of the forewing. To have distinctive features in regression models, it is recommended to use 21 x 21 –sized or 9 x 9 -sized filtered values rather than direct pixel-wise measurements.Tutkimuksessa haluttiin selvittää, voidaanko omenakääriäinen erottaa muista lähilajeista etusiivestä otetun digitaalikuvan avulla. Lisäksi haluttiin selvittää ne etusiiven alueet, joista lajitunnistus kannattaisi tehdä. Tutkimuksessa käytettiin digitaalisia RGB-kuvia kolmen Cydia-lajin lajitunnistukseen. Tutkimukseen valittiin kohdelajiksi omenan tuholainen, Cydia pomonella, sitä ulkonäöltään läheisesti muistuttava Cydia splendana sekä näistä kahdesta ulkonäöltään selvästi erottuva Cydia strobilella. Etusiivistä valittiin alueet, joissa tekstipohjaisen tiedon perusteella sijaitsivat tyypilliset lajituntomerkit. Tutkimukseen otetuista 12 etusiivestä määritettiin 6 aluetta, joista kaikista määritettiin 168 piirrettä. Piirteisiin kuului muun muassa paikallisia pikselikohtaisia intensiteettejä sekä suodatettuja mittaustuloksia. Piirteiden määrän vähentämiseksi käytettiin askeltavan regressioanalyysin algoritmeja. Valittujen piirteiden perusteella muodostettiin lineaarisia malleja, jotka testattiin lineaarisella regressioanalyysillä ja hierarkisella kokoavalla ryvästyksellä. Tutkimuksen perusteella Cydia pomonella –laji pystytään erottamaan kahdesta muusta Cydia-suvun lajista etusiivistä otettujen digitaalikuvien perusteella. Kaikkien kolmen Cydia –suvun lajin lajitunnistus oli luotettava, kun kuvien 6 tutkittua aluetta otettiin mukaan analyyseihin. Cydia pomonellan etusiivet pystyttiin erottamaan kahdesta muusta Cydia –suvun lajin etusiivistä myös vain kolmen alueen perusteella. Lajitunnistus kannattaa tehdä siiven keskiosan juovikkaan alueen sekä siiven päädyssä olevan pronssinvärisen ovaalin alueen perusteella, mutta luultavasti ei siiven keskiosan tummanruskean viirun perusteella. Erottelevimmat piirteet saatiin 21 x 21 ja 9 x 9 –kokoisilla suotimilla suodatetuista alueista, jotka selittivät paremmin lajien välistä eroa kuin pikselikohtaiset intensiteetit

Three-dimensional quantitative characterization of grapes morphology and possible relation with grey mould susceptibility

Grey mould is one of the most important diseases of grapevine in the Mediterranean regions caused by the fungi Botrytis cinerea. Many factors are responsible for this disease among them, the morphology of grapes plays a crucial role in grey mould infection. The grapes with highly compact berries are the most susceptible to infection. The common methods applied to evaluate the compactness of grapes cannot apply to grapevine bunches from the same variety. Therefore, novel methods are used to detect compactness by image processing analyses such as photogrammetry for 3D model reconstruction. This study proposes an alternative analysis of bunch morphology and compaction assessment based on virtual 3D models. Seventeen Pinot Gris clones and six Pinot Noir clones were manually collected at harvest time, and the grey mould severity evaluation was carried out in the field. All the grapes were photographed at different angulations, and the 3D model reconstruction was performed by the photogrammetry technique. Several measures and indexes were extracted from each bunch. Principal component analysis (PCA) and two multiple linear regression models (MLR) were applied to identify the descriptors of the clones most related to grey mould infection. The first model assessed the correlation between the grey mould severity and the descriptors from the 2D analysis, while the second model analyzed both descriptors from the 2D and 3D analysis. The 3D MLR presented higher performances than the 2D MLR. The R-square value (R2) and the root mean square error (RMSE) were compared between models. For Pinot Gris, the R2 rose from 0.656 to 0.838, moving from the 2D to the 3D MLR, while the RMSE decreased from 1.713 to 1.175. In Pinot Noir, the 2D model did not provide sufficient robustness, while the proposed MLR estimated R2 with 0.936 value and RMSE with 0.29 value. Additional studies were performed by analyzing the data with graphs and statistics. Consequently, the most significant traits include the estimated empty volume, the width of the grape, weight, volume, shape, and the ratio between surface and height.Grey mould is one of the most important diseases of grapevine in the Mediterranean regions caused by the fungi Botrytis cinerea. Many factors are responsible for this disease among them, the morphology of grapes plays a crucial role in grey mould infection. The grapes with highly compact berries are the most susceptible to infection. The common methods applied to evaluate the compactness of grapes cannot apply to grapevine bunches from the same variety. Therefore, novel methods are used to detect compactness by image processing analyses such as photogrammetry for 3D model reconstruction. This study proposes an alternative analysis of bunch morphology and compaction assessment based on virtual 3D models. Seventeen Pinot Gris clones and six Pinot Noir clones were manually collected at harvest time, and the grey mould severity evaluation was carried out in the field. All the grapes were photographed at different angulations, and the 3D model reconstruction was performed by the photogrammetry technique. Several measures and indexes were extracted from each bunch. Principal component analysis (PCA) and two multiple linear regression models (MLR) were applied to identify the descriptors of the clones most related to grey mould infection. The first model assessed the correlation between the grey mould severity and the descriptors from the 2D analysis, while the second model analyzed both descriptors from the 2D and 3D analysis. The 3D MLR presented higher performances than the 2D MLR. The R-square value (R2) and the root mean square error (RMSE) were compared between models. For Pinot Gris, the R2 rose from 0.656 to 0.838, moving from the 2D to the 3D MLR, while the RMSE decreased from 1.713 to 1.175. In Pinot Noir, the 2D model did not provide sufficient robustness, while the proposed MLR estimated R2 with 0.936 value and RMSE with 0.29 value. Additional studies were performed by analyzing the data with graphs and statistics. Consequently, the most significant traits include the estimated empty volume, the width of the grape, weight, volume, shape, and the ratio between surface and height

Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization

Nature-inspired algorithms have a great popularity in the current scientific community, being the focused scope of many research contributions in the literature year by year. The rationale behind the acquired momentum by this broad family of methods lies on their outstanding performance evinced in hundreds of research fields and problem instances. This book gravitates on the development of nature-inspired methods and their application to stochastic, dynamic and robust optimization. Topics covered by this book include the design and development of evolutionary algorithms, bio-inspired metaheuristics, or memetic methods, with empirical, innovative findings when used in different subfields of mathematical optimization, such as stochastic, dynamic, multimodal and robust optimization, as well as noisy optimization and dynamic and constraint satisfaction problems

A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem

[EN] The counterfort retaining wall is one of the most frequent structures used in civil engineering. In this structure, optimization of cost and CO2 emissions are important. The first is relevant in the competitiveness and efficiency of the company, the second in environmental impact. From the point of view of computational complexity, the problem is challenging due to the large number of possible combinations in the solution space. In this article, a k-means cuckoo search hybrid algorithm is proposed where the cuckoo search metaheuristic is used as an optimization mechanism in continuous spaces and the unsupervised k-means learning technique to discretize the solutions. A random operator is designed to determine the contribution of the k-means operator in the optimization process. The best values, the averages, and the interquartile ranges of the obtained distributions are compared. The hybrid algorithm was later compared to a version of harmony search that also solved the problem. The results show that the k-mean operator contributes significantly to the quality of the solutions and that our algorithm is highly competitive, surpassing the results obtained by harmony search.The first author was supported by the Grant CONICYT/FONDECYT/INICIACION/11180056, the other two authors were supported by the Spanish Ministry of Economy and Competitiveness, along with FEDER funding (Project: BIA2017-85098-R).García, J.; Yepes, V.; Martí Albiñana, JV. (2020). A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem. Mathematics. 8(4):1-22. https://doi.org/10.3390/math8040555S12284García, J., Altimiras, F., Peña, A., Astorga, G., & Peredo, O. (2018). A Binary Cuckoo Search Big Data Algorithm Applied to Large-Scale Crew Scheduling Problems. Complexity, 2018, 1-15. doi:10.1155/2018/8395193García, J., Moraga, P., Valenzuela, M., Crawford, B., Soto, R., Pinto, H., … Astorga, G. (2019). A Db-Scan Binarization Algorithm Applied to Matrix Covering Problems. Computational Intelligence and Neuroscience, 2019, 1-16. doi:10.1155/2019/3238574Al-Madi, N., Faris, H., & Mirjalili, S. (2019). Binary multi-verse optimization algorithm for global optimization and discrete problems. International Journal of Machine Learning and Cybernetics, 10(12), 3445-3465. doi:10.1007/s13042-019-00931-8Kim, M., & Chae, J. (2019). Monarch Butterfly Optimization for Facility Layout Design Based on a Single Loop Material Handling Path. Mathematics, 7(2), 154. doi:10.3390/math7020154García, J., Crawford, B., Soto, R., & Astorga, G. (2019). A clustering algorithm applied to the binarization of Swarm intelligence continuous metaheuristics. Swarm and Evolutionary Computation, 44, 646-664. doi:10.1016/j.swevo.2018.08.006García, J., Lalla-Ruiz, E., Voß, S., & Droguett, E. L. (2020). Enhancing a machine learning binarization framework by perturbation operators: analysis on the multidimensional knapsack problem. International Journal of Machine Learning and Cybernetics, 11(9), 1951-1970. doi:10.1007/s13042-020-01085-8García, J., Moraga, P., Valenzuela, M., & Pinto, H. (2020). A db-Scan Hybrid Algorithm: An Application to the Multidimensional Knapsack Problem. Mathematics, 8(4), 507. doi:10.3390/math8040507Saeheaw, T., & Charoenchai, N. (2018). A comparative study among different parallel hybrid artificial intelligent approaches to solve the capacitated vehicle routing problem. International Journal of Bio-Inspired Computation, 11(3), 171. doi:10.1504/ijbic.2018.091704Valdez, F., Castillo, O., Jain, A., & Jana, D. K. (2019). Nature-Inspired Optimization Algorithms for Neuro-Fuzzy Models in Real-World Control and Robotics Applications. Computational Intelligence and Neuroscience, 2019, 1-2. doi:10.1155/2019/9128451Penadés-Plà, V., García-Segura, T., & Yepes, V. (2020). Robust Design Optimization for Low-Cost Concrete Box-Girder Bridge. Mathematics, 8(3), 398. doi:10.3390/math8030398García-Segura, T., Yepes, V., Frangopol, D. M., & Yang, D. Y. (2017). Lifetime reliability-based optimization of post-tensioned box-girder bridges. Engineering Structures, 145, 381-391. doi:10.1016/j.engstruct.2017.05.013Yepes, V., Martí, J. V., & García, J. (2020). Black Hole Algorithm for Sustainable Design of Counterfort Retaining Walls. Sustainability, 12(7), 2767. doi:10.3390/su12072767Marti-Vargas, J. R., Ferri, F. J., & Yepes, V. (2013). Prediction of the transfer length of prestressing strands with neural networks. Computers and Concrete, 12(2), 187-209. doi:10.12989/cac.2013.12.2.187Fu, W., Tan, J., Zhang, X., Chen, T., & Wang, K. (2019). Blind Parameter Identification of MAR Model and Mutation Hybrid GWO-SCA Optimized SVM for Fault Diagnosis of Rotating Machinery. Complexity, 2019, 1-17. doi:10.1155/2019/3264969Sierra, L. 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(2018). A hybrid metaheuristic and kernel intuitionistic fuzzy c-means algorithm for cluster analysis. Applied Soft Computing, 67, 299-308. doi:10.1016/j.asoc.2018.02.039Singh Mann, P., & Singh, S. (2017). Energy efficient clustering protocol based on improved metaheuristic in wireless sensor networks. Journal of Network and Computer Applications, 83, 40-52. doi:10.1016/j.jnca.2017.01.031Rosa, R. de A., Machado, A. M., Ribeiro, G. M., & Mauri, G. R. (2016). A mathematical model and a Clustering Search metaheuristic for planning the helicopter transportation of employees to the production platforms of oil and gas. Computers & Industrial Engineering, 101, 303-312. doi:10.1016/j.cie.2016.09.006Faris, H., Mirjalili, S., & Aljarah, I. (2019). Automatic selection of hidden neurons and weights in neural networks using grey wolf optimizer based on a hybrid encoding scheme. International Journal of Machine Learning and Cybernetics, 10(10), 2901-2920. doi:10.1007/s13042-018-00913-2De Rosa, G. 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K-Means clustering and neural network for object detecting and identifying abnormality of brain tumor. Soft Computing, 23(19), 9083-9096. doi:10.1007/s00500-018-3618-7Abdel-Basset, M., Wang, G.-G., Sangaiah, A. K., & Rushdy, E. (2017). Krill herd algorithm based on cuckoo search for solving engineering optimization problems. Multimedia Tools and Applications, 78(4), 3861-3884. doi:10.1007/s11042-017-4803-xChi, R., Su, Y., Zhang, D., Chi, X., & Zhang, H. (2017). A hybridization of cuckoo search and particle swarm optimization for solving optimization problems. Neural Computing and Applications, 31(S1), 653-670. doi:10.1007/s00521-017-3012-xLi, J., Xiao, D., Lei, H., Zhang, T., & Tian, T. (2020). Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location. Mathematics, 8(2), 149. doi:10.3390/math8020149Pan, J.-S., Song, P.-C., Chu, S.-C., & Peng, Y.-J. (2020). 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