Comparison of decision tree methods in classification of researcher’s cognitive styles in academic environment

In today's internet world, providing feedbacks to users based on what they need and their knowledge is essential. Classification is one of the data mining methods used to mine large data. There are several classification techniques used to solve classification problems. In this article, classification techniques are used to classify researchers as “Expert” and “Novice” based on cognitive styles factors in academic settings using several Decision Tree techniques. Decision Tree is the suitable technique to choose for classification in order to categorize researchers as “Expert” and “Novice” because it produces high accuracy. Environment Waikato Knowledge Analysis (WEKA) is an open source tool used for classification. Using WEKA, the Random Forest technique was selected as the best method because it provides accuracy of 92.72728. Based on these studies, most researchers have a better knowledge of their own domain and their problems and show more competencies in their information seeking behavior compared to novice researchers. This is because the “experts” have a clear understanding of their research problems and is more efficient in information searching activities. Classification techniques are implemented as a digital library search engine because it can help researchers to have the best response according to their demand

On the Use of Explainable Artificial Intelligence for the Differential Diagnosis of Pigmented Skin Lesions

En los últimos años, la Inteligencia Artificial Explicable (XAI) ha atraído la atención en la analítica de datos, ya que muestra un gran potencial en la interpretación de los resultados de complejos modelos de aprendizaje automático en la aplicación de problemas médicos. Se trata de que el resultado de las aplicaciones basadas en el aprendizaje automático deben ser comprendidos por los usuarios finales, especialmente en el contexto de los datos médicos, donde las decisiones deben tomarse cuidadosamente. decisiones. Como tal, se han realizado muchos esfuerzos para explicar el resultado de un modelo complejo de aprendizaje profundo en procesos de reconocimiento y clasificación de y clasificación de imágenes, como en el caso del cáncer de melanoma. Este representa un primer intento (hasta donde sabemos) de investigar experimental y técnicamente la explicabilidad de los métodos modernos de XAI modernos de XAI: explicaciones de modelos de diagnóstico interpretables locales (LIME) y Shapley Additive exPlanations (SHAP), en términos de reproducibilidad de resultados y el tiempo de ejecución en un conjunto de datos de clasificación de imágenes de melanoma. Este artículo muestra que los métodos XAI proporcionan ventajas en la interpretación de los resultados del modelo en la clasificación de imágenes de melanoma. interpretación de los resultados del modelo en la clasificación de imágenes de melanoma. Concretamente, LIME se comporta mejor que el explicador de gradiente SHAP en términos de reproducibilidad y tiempo de ejecución.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Ensemble-based genetic algorithm explainer with automized image segmentation: A case study on melanoma detection dataset

Explainable Artificial Intelligence (XAI) makes AI understandable to the human user particularly when the model is complex and opaque. Local Interpretable Model-agnostic Explanations (LIME) has an image explainer package that is used to explain deep learning models. The image explainer of LIME needs some parameters to be manually tuned by the expert in advance, including the number of top features to be seen and the number of superpixels in the segmented input image. This parameter tuning is a time-consuming task. Hence, with the aim of developing an image explainer that automizes image segmentation, this paper proposes Ensemblebased Genetic Algorithm Explainer (EGAE) for melanoma cancer detection that automatically detects and presents the informative sections of the image to the user. EGAE has three phases. First, the sparsity of chromosomes in GAs is determined heuristically. Then, multiple GAs are executed consecutively. However, the difference between these GAs are in different number of superpixels in the input image that result in different chromosome lengths. Finally, the results of GAs are ensembled using consensus and majority votings. This paper also introduces how Euclidean distance can be used to calculate the distance between the actual explanation (delineated by experts) and the calculated explanation (computed by the explainer) for accuracy measurement. Experimental results on a melanoma dataset show that EGAE automatically detects informative lesions, and it also improves the accuracy of explanation in comparison with LIME efficiently. The python codes for EGAE, the ground truths delineated by clinicians, and the melanoma detection dataset are available at https://github.com/KhaosResearch/EGAEThis work has been partially funded by grant PID2020-112540RBC41 (funded by MCIN/AEI/10.13039/501100011033/, Spain), AETHERUMA, Spain (A smart data holistic approach for context-aware data analytics: semantics and context exploitation). Funding for open access charge: Universidad de Málaga/CBUA. Additionally, we thank Dr. Miguel Ángel Berciano Guerrero from Unidad de Oncología Intercentros, Hospitales Univesitarios Regional Virgen de la Victoria de Málaga, and Instituto de Investigaciones Biomédicas (IBIMA), Málaga, Spain, for his support in images selection and general medical orientation in the particular case of Melanoma

Contrasting the mechanical and metallurgical properties of laser welded and gas tungsten arc welded S500MC steel

S500MC steel is a grade of high-strength low-alloy steel (HSLA) which is widely used in the automotive industry and for agricultural machinery and equipment. Considering properties of this alloy, selection of the welding process and parameters becomes essential to ensure that HSLA assemblies meet specific service requirements. In this work, mechanical and metallurgical properties of S500MC steel produced by autogenous laser beam welding (LBW) and automatic gas tungsten arc welding (GTAW) were compared. Tensile testing, metallography, hardness testing, and fractographic analysis were performed on the welded specimens, revealing that the heat input by these welding processes caused significant microstructural changes within the joints. In LBW samples, the heat input about 10 times lower than that in GTAW produced a finer microstructure, narrower fusion zone width, and smaller heat-affected zone. All fractures of the GTAW specimens occurred in the base metal, while all fractures of the LBW specimens occurred in the weld zone, both regardless of the heat input. GTAW joints exhibited higher mechanical properties (even higher than those obtained in the base metal) as compared to LBW joints

Modeling activity diagram to colored petri net for validation and verification based on non functional parameters

UML is one of the modeling tools which gains wide area of usage in developing softwares. It consists of many diagrams which help developers of a software to produce better product. One of its diagrams is called Activity Diagram. It is a deliverable which is usually produced in the analysis phase of software. It consists of many important benefits, yet it has weaknesses too. One important thing which the current Activity Diagram is unable to do it is that it can not be validated and verified. The current Activity Diagram is a functional diagram and to extract non functional parameters from functional diagram is impossible but through modeling it to colored Petri net and by using the formalism of colored Petri net we may able to verify and validate the Activity Diagram. The ultimate outcome of this study would be handful information to manage the current mentioned Activity Diagram's weakness. Moreover a computer tool is provided called ADET to validate and verify the activity diagram

QoS measurement of workflow-based web service compositions using colored petri net

Workflow-based web service compositions (WB-WSCs) is one of the main composition categories in service oriented architecture (SOA). Eflow, polymorphic process model (PPM), and business process execution language (BPEL) are the main techniques of the category of WB-WSCs. Due to maturity of web services, measuring the quality of composite web services being developed by different techniques becomes one of the most important challenges in today's web environments. Business should try to provide good quality regarding the customers' requirements to a composed web service. Thus, quality of service (QoS) which refers to nonfunctional parameters is important to be measured since the quality degree of a certain web service composition could be achieved. This paper tried to find a deterministic analytical method for dependability and performance measurement using Colored Petri net (CPN) with explicit routing constructs and application of theory of probability. A computer tool called WSET was also developed for modeling and supporting QoS measurement through simulation

Automatic frequency-based feature selection using discrete weighted evolution strategy

High dimensional datasets usually suffer from curse of dimensionality which may increase the classification time and decrease the classification accuracy beyond a certain dimensionality. Thus, feature selection is used to discard redundant features for improving classification. Nonetheless, there is not a single feature selection method which could deal with all datasets. Thus, this paper proposes an automatic hybrid feature selection incorporating both filter and wrapper methods called Extended Mutual Congestion-Discrete Weighted Evolution Strategy (EMC-DWES). First, Extended Mutual Con-gestion (EMC) is proposed as a frequency-based filter ranker to discard irrelevant and redundant features using intrinsic statistics of features. Second, Discrete Weighted Evolution Strategy (DWES) is applied on the remaining features selected by EMC to perform the final automatic feature selection within a wrapper method. DWES clusters the features and applies mutation both to select the most relevant feature in each cluster at a time and to avoid selecting redundant features simultaneously through assigning greater weights to most informative clusters. The performance of EMC-DWES (in maximizing classification accuracy and minimizing the selected subset length) is investigated using benchmark high dimensional medical datasets including Covid-19. Likewise, the superiority of EMC-DWES in comparison with state-of-the-art is also evaluated in all datasets. The implementation of EMC-DWES is available on https://github.com/KhaosResearch/EMC-DWES.(c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)