Automated Lung Disease Detection and Classification Using Quantum Glowworm Swarm Optimizer with Quasi Recurrent Neural Network on Chest X-Ray Images

Lung diseases or otherwise called respiratory diseases are airborne diseases that affect the lungs and the other tissues of the lungs. Tuberculosis, Coronavirus Disease 2019 (COVID-19), and Pneumonia are a few instances of lung diseases. If the lung disease is diagnosed and treated in the initial stage, the chances of recovery rate and long-term survival rates can be increased. Usually, lung disease is identified by Chest X-Ray (CXR) image examination, skin test, sputum sample test, Computed Tomography (CT) scan examination, and blood test. Because of its non-invasive and convenient evaluation for overall outcomes of the chest situation, Lung disease can be detected by specialized radiologists on CXR images. In recent times, Deep Learning (DL) applies to medical images for disease detection and has proved an effective technique for detecting disease. The recent advancement of DL supports the detection and classification of lung diseases in medicinal imaging. This article presents an Automated Lung Disease Detection Using Quantum Glowworm Swarm Optimization with Quasi Recurrent Neural Network (QGSO-QRNN) model on CXR imaging. The presented QGSO-QRNN technique focuses on the identification of lung diseases using DL concepts. To accomplish this, the presented QGSO-QRNN technique initially performs image pre-processing by the use of the Gaussian Filtering (GF) technique. Besides, the Faster SqueezeNet approach is exploited for feature vector generation. Finally, the QRNN model is applied for precise classification of lung diseases with the QGSO technique as a hyperparameter optimizer. The investigational assessment of the QGSO-QRNN technique is examined by employing standard medical datasets and the outputs display the promising performance of the QGSO-QRNN technique over other existing techniques by means of diverse measures

A comprehensive review of swarm optimization algorithms

Many swarm optimization algorithms have been introduced since the early 60’s, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained, and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches

Applied (Meta)-Heuristic in Intelligent Systems

Engineering and business problems are becoming increasingly difficult to solve due to the new economics triggered by big data, artificial intelligence, and the internet of things. Exact algorithms and heuristics are insufficient for solving such large and unstructured problems; instead, metaheuristic algorithms have emerged as the prevailing methods. A generic metaheuristic framework guides the course of search trajectories beyond local optimality, thus overcoming the limitations of traditional computation methods. The application of modern metaheuristics ranges from unmanned aerial and ground surface vehicles, unmanned factories, resource-constrained production, and humanoids to green logistics, renewable energy, circular economy, agricultural technology, environmental protection, finance technology, and the entertainment industry. This Special Issue presents high-quality papers proposing modern metaheuristics in intelligent systems

Warpage optimisation on the moulded part with straight drilled and conformal cooling channels using Response Surface Methodology (RSM), Glowworm Swarm Optimisation (GSO) and Genetic Algorithm (GA) Optimisation Approaches

It is quite challenging to control both quality and productivity of products produced using injection molding process. Although many previous researchers have used different types of optimisation approaches to obtain the best configuration of parameters setting to control the quality of the molded part, optimisation approaches in maximising the performance of cooling channels to enhance the process productivity by decreasing the mould cycle time remain lacking. In this study, optimisation approaches namely Response Surface Methodology (RSM), Genetic Algorithm (GA) and Glowworm Swarm Optimisation (GSO) were employed on front panel housing moulded using Acrylonitrile Butadiene Styrene (ABS). Each optimisation method was analysed for both straight drilled and Milled Groove Square Shape (MGSS) conformal cooling channel moulds. Results from experimental works showed that, the performance of MGSS conformal cooling channels could be enhanced by employing the optimisation approach. Therefore, this research provides useful scientific knowledge and an alternative solution for the plastic injection moulding industry to improve the quality of moulded parts in terms of deformation using the proposed optimisation approaches in the used of conformal cooling channels mould

Efficient design of post-tensioned concrete box-girder road bridges based on sustainable multi-objective criteria

[EN] Bridges, as an important component of infrastructure, are expected to meet all the requirements for a modern society. Traditionally, the primary aim in bridge design has been to achieve the lowest cost while guaranteeing the structural efficiency. However, concerns regarding building a more sustainable future have change the priorities of society. Ecological and durable structures are increasingly demanded. Under these premises, heuristic optimization methods provide an effective alternative to structural designs based on experience. The emergence of new materials, structural designs and sustainable criteria motivate the need to create a methodology for the automatic and accurate design of a real post-tensioned concrete bridge that considers all these aspects. For the first time, this thesis studies the efficient design of post-tensioned concrete box-girder road bridges from a sustainable point of view. This research integrates environmental, safety and durability criteria into the optimum design of the bridge. The methodology proposed provides multiple trade-off solutions that hardly increase the cost and achieve improved safety and durability. Likewise, this approach quantifies the sustainable criteria in economic terms, and evaluates the effect of these criteria on the best values of the variables. In this context, a multi-objective optimization is formulated to provide multiple trade-off and high-performing solutions that balance economic, ecologic and societal goals. An optimization design program selects the best geometry, concrete type, reinforcement and post-tensioning steel that meet the objectives selected. A three-span continuous box-girder road bridge located in a coastal region is selected for a case study. This approach provides vital knowledge about this type of bridge in the sustainable context. The life-cycle perspective has been included through a lifetime performance evaluation that models the bridge deterioration process due to chloride-induced corrosion. The economic, environmental and societal impacts of maintenance actions required to extend the service life are examined. Therefore, the proposed goals for an efficient design have been switch from initial stage to life-cycle consideration. Faced with the large computational time of multi-objective optimization and finite-element analysis, artificial neural networks (ANNs) are integrated in the proposed methodology. ANNs are trained to predict the structural response based on the design variables, without the need to analyze the bridge response. The multi-objective optimization problem results in a set of trade-off solutions characterized by the presence of conflicting objectives. The final selection of preferred solutions is simplified by a decision-making technique. A rational technique converts a verbal pairwise comparison between criteria with a degree of uncertainty into numerical values that guarantee the consistency of judgments. This thesis gives a guide for the sustainable design of concrete structures. The use of the proposed approach leads to designs with lower life-cycle cost and emissions compared to general design approaches. Both bridge safety and durability can be improved with a little cost increment by choosing the correct design variables. In addition, this methodology is applicable to any type of structure and material.[ES] Los puentes, como parte importante de una infraestructura, se espera que reúnan todos los requisitos de una sociedad moderna. Tradicionalmente, el objetivo principal en el diseño de puentes ha sido lograr el menor coste mientras se garantiza la eficiencia estructural. Sin embargo, la preocupación por construir un futuro más sostenible ha provocado un cambio en las prioridades de la sociedad. Estructuras más ecológicas y duraderas son cada vez más demandadas. Bajo estas premisas, los métodos de optimización heurística proporcionan una alternativa eficaz a los diseños estructurales basados en la experiencia. La aparición de nuevos materiales, diseños estructurales y criterios sostenibles motivan la necesidad de crear una metodología para el diseño automático y preciso de un puente real de hormigón postesado que considere todos estos aspectos. Por primera vez, esta tesis estudia el diseño eficiente de puentes de hormigón postesado con sección en cajón desde un punto de vista sostenible. Esta investigación integra criterios ambientales, de seguridad estructural y durabilidad en el diseño óptimo del puente. La metodología propuesta proporciona múltiples soluciones que apenas encarecen el coste y mejoran la seguridad y durabilidad. Al mismo tiempo, se cuantifica el enfoque sostenible en términos económicos, y se evalúa el efecto que tienen dichos criterios en el valor óptimo de las variables. En este contexto, se formula una optimización multiobjetivo que proporciona soluciones eficientes y de compromiso entre los criterios económicos, ecológicos y sociales. Un programa de optimización del diseño selecciona la mejor combinación de geometría, tipo de hormigón, armadura y postesado que cumpla con los objetivos seleccionados. Se ha escogido como caso de estudio un puente continuo en cajón de tres vanos situado en la costa. Este método proporciona un mayor conocimiento sobre esta tipología de puentes desde un punto de vista sostenible. Se ha estudiado el ciclo de vida a través de la evaluación del deterioro estructural del puente debido al ataque por cloruros. Se examina el impacto económico, ambiental y social que produce el mantenimiento necesario para extender la vida útil del puente. Por lo tanto, los objetivos propuestos para un diseño eficiente han sido trasladados desde la etapa inicial hasta la consideración del ciclo de vida. Para solucionar el problema del elevado tiempo de cálculo debido a la optimización multiobjetivo y el análisis por elementos finitos, se han integrado redes neuronales en la metodología propuesta. Las redes neuronales son entrenadas para predecir la respuesta estructural a partir de las variables de diseño, sin la necesidad de analizar el puente. El problema de optimización multiobjetivo se traduce en un conjunto de soluciones de compromiso que representan objetivos contrapuestos. La selección final de las soluciones preferidas se simplifica mediante una técnica de toma de decisiones. Una técnica estructurada convierte los juicios basados en comparaciones por pares de elementos con un grado de incertidumbre en valores numéricos que garantizan la consistencia de dichos juicios. Esta tesis proporciona una guía que extiende y mejora las recomendaciones sobre el diseño de estructuras de hormigón dentro del contexto de desarrollo sostenible. El uso de la metodología propuesta lleva a diseños con menor coste y emisiones del ciclo de vida, comparado con diseños que siguen metodologías generales. Los resultados demuestran que mediante una correcta elección del valor de las variables se puede mejorar la seguridad y durabilidad del puente con un pequeño incremento del coste. Además, esta metodología es aplicable a cualquier tipo de estructura y material.[CA] Els ponts, com a part important d'una infraestructura, s'espera que reunisquen tots els requisits d'una societat moderna. Tradicionalment, l'objectiu principal en el disseny de ponts ha sigut aconseguir el menor cost mentres es garantix l'eficiència estructural. No obstant això, la preocupació per construir un futur més sostenible ha provocat un canvi en les prioritats de la societat. Estructures més ecològiques i durables són cada vegada més demandades. Davall estes premisses, els mètodes d'optimització heurística proporcionen una alternativa eficaç als dissenys estructurals basats en l'experiència. L'aparició de nous materials, dissenys estructurals i criteris sostenibles motiven la necessitat de crear una metodologia per al disseny automàtic i precís d'un pont real de formigó posttesat que considere tots estos aspectos. Per primera vegada, esta tesi estudia el disseny eficient de ponts de formigó posttesat amb secció en calaix des d'un punt de vista sostenible. Esta investigació integra criteris ambientals, de seguretat estructural i durabilitat en el disseny òptim del pont. La metodologia proposada proporciona múltiples solucions que a penes encarixen el cost i milloren la seguretat i durabilitat. Al mateix temps, es quantifica l'enfocament sostenible en termes econòmics, i s'avalua l'efecte que tenen els dits criteris en el valor òptim de les variables. En este context, es formula una optimització multiobjetivo que proporciona solucions eficients i de compromís entre els criteris econòmics, ecològics i socials. Un programa d'optimització del disseny selecciona la millor geometria, tipus de formigó, armadura i posttesat que complisquen amb els objectius seleccionats. S'ha triat com a cas d'estudi un pont continu en calaix de tres vans situat en la costa. Este mètode proporciona un major coneixement sobre esta tipologia de ponts des d'un punt de vista sostenible. S'ha estudiat el cicle de vida a través de l'avaluació del deteriorament estructural del pont a causa de l'atac per clorurs. S'examina l'impacte econòmic, ambiental i social que produïx el manteniment necessari per a estendre la vida útil del pont. Per tant, els objectius proposats per a un disseny eficient han sigut traslladats des de l'etapa inicial fins a la consideració del cicle de vida. Per a solucionar el problema de l'elevat temps de càlcul degut a l'optimització multiobjetivo i l'anàlisi per elements finits, s'han integrat xarxes neuronals en la metodologia proposada. Les xarxes neuronals són entrenades per a predir la resposta estructural a partir de les variables de disseny, sense la necessitat d'analitzar el pont. El problema d'optimització multiobjetivo es traduïx en un conjunt de solucions de compromís que representen objectius contraposats. La selecció final de les solucions preferides se simplifica per mitjà d'una tècnica de presa de decisions. Una tècnica estructurada convertix els juís basats en comparacions per parells d'elements amb un grau d'incertesa en valors numèrics que garantixen la consistència dels dits juís. Esta tesi proporciona una guia que estén i millora les recomanacions sobre el disseny d'estructures de formigó dins del context de desenrotllament sostenible. L'ús de la metodologia proposada porta a dissenys amb menor cost i emissions del cicle de vida, comparat amb dissenys que seguixen metodologies generals. Els resultats demostren que per mitjà d'una correcta elecció del valor de les variables es pot millorar la seguretat i durabilitat del pont amb un xicotet increment del cost. A més, esta metodologia és aplicable a qualsevol tipus d'estructura i material.García Segura, T. (2016). Efficient design of post-tensioned concrete box-girder road bridges based on sustainable multi-objective criteria [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/73147TESI

Hybrid artificial fish and glowworm swarm optimization algorithm for electrical discharge machining of titanium alloy

Electrical discharge machining (EDM) is a non-traditional machining process widely used to machine geometrically complex and hard materials. In EDM, selection of optimal EDM parameters is important to have high quality products and increase productivity. However, one of the major issues is to obtain better machining performance at optimal value of these machining parameters. Modelling and optimization of EDM parameters have been considered to identify optimal EDM parameters that would lead to better EDM performance. Due to the complexity and uncertainty of the machining process, computational approaches have been implemented to solve the EDM problem. Thus, this study conducted a comprehensive investigation concerning the influence of EDM parameters on material removal rate (MRR), surface roughness (Ra) and dimensional accuracy (DA) through an experimental design. The experiment was performed based on full factorial design of experiment (DOE) with added center points of pulse on time (Ton), pulse off time (Toff), peak current (Ip) and servo voltage (Sv). In the EDM optimization, glowworm swarm optimization (GSO) algorithm was implemented. However, previous works indicated that GSO algorithm has always been trapped in the local optima solution and is slow in convergence. Therefore, this study developed a new hybrid artificial fish and glowworm swarm optimization (AF-GSO) algorithm to overcome the weaknesses of GSO algorithm in order to have a better EDM performance. For the modeling process, four types of regression models, namely multiple linear regression (MLR), two factor interaction (2FI), multiple polynomial regression (MPR) and stepwise regression (SR) were developed. These regression models were implemented in the optimization process as an objective function equation. Analysis of the optimization proved that AF-GSO algorithm has successfully outperformed the standard GSO algorithm. 2FI model of AF-GSO optimization for MRR and DA gave optimal solutions of 0.0042g/min and 0.00129%, respectively. On the other hand, the SR model for Ra of AF-GSO optimization gave the optimal solution of 1.8216p,s. Overall, it can be concluded that AF-GSO algorithm has successfully improved the quality and productivity of the EDM problems