Component-wise analysis of metaheuristic algorithms for novel fuzzy-meta classifier

Metaheuristic research has proposed promising results in science, business, and engineering problems. But, mostly high-level analysis is performed on metaheuristic performances. This leaves several critical questions unanswered due to black-box issue that does not reveal why certain metaheuristic algorithms performed better on some problems and not on others. To address the significant gap between theory and practice in metaheuristic research, this study proposed in-depth analysis approach using component-view of metaheuristic algorithms and diversity measurement for determining exploration and exploitation abilities. This research selected three commonly used swarm-based metaheuristic algorithms – Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Cuckoo Search (CS) – to perform component-wise analysis. As a result, the study able to address premature convergence problem in PSO, poor exploitation in ABC, and imbalanced exploration and exploitation issue in CS. The proposed improved PSO (iPSO), improved ABC (iABC), and improved CS (iCS) outperformed standard algorithms and variants from existing literature, as well as, Grey Wolf Optimization (GWO) and Animal Migration Optimization (AMO) on ten numerical optimization problems with varying modalities. The proposed iPSO, iABC, and iCS were then employed on proposed novel Fuzzy-Meta Classifier (FMC) which offered highly reduced model complexity and high accuracy as compared to Adaptive Neuro-Fuzzy Inference System (ANFIS). The proposed three-layer FMC produced efficient rules that generated nearly 100% accuracies on ten different classification datasets, with significantly reduced number of trainable parameters and number of nodes in the network architecture, as compared to ANFIS

Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications

This book is a Special Issue Reprint edited by Prof. Massimo Vitelli and Dr. Luigi Costanzo. It contains original research articles covering, but not limited to, the following topics: maximum power point tracking techniques; forecasting techniques; sizing and optimization of PV components and systems; PV modeling; reconfiguration algorithms; fault diagnosis; mismatching detection; decision processes for grid operators

Logic mining with hybridized 3-satisfiability fuzzy logic and harmony search algorithm in Hopfield neural network for Covid-19 death cases

Since the beginning of the Covid-19 infections in December 2019, the virus has emerged as the most lethally contagious in world history. In this study, the Hopfield neural network and logic mining technique merged to extract data from a model to provide insight into the link between factors influencing the Covid-19 datasets. The suggested technique uses a 3-satisfiability-based reverse analysis (3SATRA) and a hybridized Hopfield neural network to identify the relationships relating to the variables in a set of Covid-19 data. The list of data is to identify the relationships between the key characteristics that lead to a more prolonged time of death of the patients. The learning phase of the hybridized 3-satisfiability (3SAT) Hopfield neural network and the reverse analysis (RA) method has been optimized using a new method of fuzzy logic and two metaheuristic algorithms: Genetic and harmony search algorithms. The performance assessment metrics, such as energy analysis, error analysis, computational time, and accuracy, were computed at the end of the algorithms. The multiple performance metrics demonstrated that the 3SATRA with the fuzzy logic metaheuristic algorithm model outperforms other logic mining models. Furthermore, the experimental findings have demonstrated that the best-induced logic identifies important variables to detect critical patients that need more attention. In conclusion, the results validate the efficiency of the suggested approach, which occurs from the fact that the new version has a positive effect

Women in Science 2016

Women in Science 2016 summarizes research done by Smith College’s Summer Research Fellowship (SURF) Program participants. Ever since its 1967 start, SURF has been a cornerstone of Smith’s science education. In 2016, 150 students participated in SURF (144 hosted on campus and nearby eld sites), supervised by 56 faculty mentor-advisors drawn from the Clark Science Center and connected to its eighteen science, mathematics, and engineering departments and programs and associated centers and units. At summer’s end, SURF participants were asked to summarize their research experiences for this publication.https://scholarworks.smith.edu/clark_womeninscience/1005/thumbnail.jp

Smart Urban Water Networks

This book presents the paper form of the Special Issue (SI) on Smart Urban Water Networks. The number and topics of the papers in the SI confirm the growing interest of operators and researchers for the new paradigm of smart networks, as part of the more general smart city. The SI showed that digital information and communication technology (ICT), with the implementation of smart meters and other digital devices, can significantly improve the modelling and the management of urban water networks, contributing to a radical transformation of the traditional paradigm of water utilities. The paper collection in this SI includes different crucial topics such as the reliability, resilience, and performance of water networks, innovative demand management, and the novel challenge of real-time control and operation, along with their implications for cyber-security. The SI collected fourteen papers that provide a wide perspective of solutions, trends, and challenges in the contest of smart urban water networks. Some solutions have already been implemented in pilot sites (i.e., for water network partitioning, cyber-security, and water demand disaggregation and forecasting), while further investigations are required for other methods, e.g., the data-driven approaches for real time control. In all cases, a new deal between academia, industry, and governments must be embraced to start the new era of smart urban water systems

DATA-DRIVEN ANALYTICAL MODELS FOR IDENTIFICATION AND PREDICTION OF OPPORTUNITIES AND THREATS

During the lifecycle of mega engineering projects such as: energy facilities, infrastructure projects, or data centers, executives in charge should take into account the potential opportunities and threats that could affect the execution of such projects. These opportunities and threats can arise from different domains; including for example: geopolitical, economic or financial, and can have an impact on different entities, such as, countries, cities or companies. The goal of this research is to provide a new approach to identify and predict opportunities and threats using large and diverse data sets, and ensemble Long-Short Term Memory (LSTM) neural network models to inform domain specific foresights. In addition to predicting the opportunities and threats, this research proposes new techniques to help decision-makers for deduction and reasoning purposes. The proposed models and results provide structured output to inform the executive decision-making process concerning large engineering projects (LEPs). This research proposes new techniques that not only provide reliable timeseries predictions but uncertainty quantification to help make more informed decisions. The proposed ensemble framework consists of the following components: first, processed domain knowledge is used to extract a set of entity-domain features; second, structured learning based on Dynamic Time Warping (DTW), to learn similarity between sequences and Hierarchical Clustering Analysis (HCA), is used to determine which features are relevant for a given prediction problem; and finally, an automated decision based on the input and structured learning from the DTW-HCA is used to build a training data-set which is fed into a deep LSTM neural network for time-series predictions. A set of deeper ensemble programs are proposed such as Monte Carlo Simulations and Time Label Assignment to offer a controlled setting for assessing the impact of external shocks and a temporal alert system, respectively. The developed model can be used to inform decision makers about the set of opportunities and threats that their entities and assets face as a result of being engaged in an LEP accounting for epistemic uncertainty

Applications of artificial neural networks in three agro-environmental systems: microalgae production, nutritional characterization of soils and meteorological variables management

La agricultura es una actividad esencial para los humanos, es altamente dependiente de las condiciones meteorológicas y foco de investigación e innovación con el objetivo de enfrentar diversos desafíos. El cambio climático, calentamiento global y la degradación de los ecosistemas agrícolas son sólo algunos de los problemas que los humanos enfrentamos para continuar con la esencial producción de alimentos. Buscando la innovación en el sector agrícola, se consideraron tres tópicos principales de investigación para esta tesis; la producción de microalgas, el color del suelo y la fertilidad, y la adquisición de datos meteorológicos. Estos temas tienen roles cada vez más importantes en la agricultura, especialmente bajo la incertidumbre del futuro de la producción de alimentos. Las microalgas son una interesante alternativa para la fertilización de cultivos y la sostenibilidad del suelo; mientras que los parámetros de fertilidad del suelo necesitan ser más estudiados para desarrollar métodos de análisis de menor costo y más rápidos para ayudar al manejo. La agricultura, como actividad altamente dependiente del clima, necesita de datos meteorológicos para anticipar eventos, planificar y manejar los cultivos eficientemente. Estos temas se seleccionaron con el propósito de mejorar el estado actual de la técnica, proponer nuevas alternativas basadas, principalmente, en la aplicación de redes neuronales artificiales (ANN) como una manera novedosa de resolver los problemas y generar conocimiento de aplicación directa en sistemas de cultivos. El objetivo principal de esta tesis fue generar modelos de ANNs capaces de abordar problemas relacionados con la agricultura, como una alternativa a los métodos tradicionales y más costosos empleados en el manejo, análisis y adquisición de datos en los sistemas agrarios.Departamento de Ingeniería Agrícola y ForestalDoctorado en Ciencia e Ingeniería Agroalimentaria y de Biosistema

Methods for the refinement of genome-scale metabolic networks

More accurate metabolic networks of pathogens and parasites are required to support the identification of important enzymes or transporters that could be potential targets for new drugs. The overall aim of this thesis is to contribute towards a new level of quality for metabolic network reconstruction, through the application of several different approaches. After building a draft metabolic network using an automated method, a large amount of manual curation effort is still necessary before an accurate model can be reached. PathwayBooster, a standalone software package, which I developed in Python, supports the first steps of model curation, providing easy access to enzymatic function information and a visual pathway display to enable the rapid identification of inaccuracies in the model. A major current problem in model refinement is the identification of genes encoding enzymes which are believed to be present but cannot be found using standard methods. Current searches for enzymes are mainly based on strong sequence similarity to proteins of known function, although in some cases it may be appropriate to consider more distant relatives as candidates for filling these pathway holes. With this objective in mind, a protocol was devised to search a proteome for superfamily relatives of a given enzymatic function, returning candidate enzymes to perform this function. Another, related approach tackles the problem of misannotation errors in public gene databases and their influence on metabolic models through the propagation of erroneous annotations. I show that the topological properties of metabolic networks contains useful information about annotation quality and can therefore play a role in methods for gene function assignment. An evolutionary perspective into functional changes within homologous domains opens up the possibility of integrating information from multiple genomes to support the reconstruction of metabolic models. I have therefore developed a methodology to predict functional change within a gene superfamily phylogeny