Soft computing approaches for image segmentation: a survey

Control algorithms are the most critical aspects in the successful control of civil structures subjected to earthquake and wind forces. In recent years, adaptive intelligent control algorithms are emerging as an acceptable substitute method to conventional model-based control algorithms. These algorithms mainly work on the principles of artificial intelligence (AI) and soft computing (SC) methods that make them highly efficient in controlling highly nonlinear, time-varying, and time-delayed complex civil structures. The current research probes to control algorithms, that this article set forth an inclusive state-of-the-art review of adaptive intelligent control (AIC) algorithms for vibration control of smart civil structures. First, a general introduction to adaptive intelligent control is presented along with its advantages over conventional control algorithms. Second, their classification concerning artificial intelligence and soft computing methods is provided that mainly consists of artificial neural network-based controller, brain emotional learning-based intelligent controller, replicator dynamics-based controller, multi-agent system-based controller, support vector machine-based controller, fuzzy logic control, adaptive neuro-fuzzy inference system-based controller, adaptive filters-base controller, and meta-heuristic algorithms-based hybrid controllers. Third, a brief review of these algorithms with their developments on the theory and applications is provided. Fourth, we demonstrate a summarized overview of the cited literature with a brief trend analysis is presented. Finally, this study presents an overview of these innovative AIC methods that can demonstrate future directions. The contribution of this article is the anticipation of detailed and in-depth discussion into the perspective of AI and SC-based AIC method advances that enabled practical applications in attenuating vibration response of smart civil structures. Moreover, the review demonstrates the computing advantages of AIC over conventional controllers that are important in creating the next generation of smart civil structures.

Introduction: Soft Computing approaches are becoming increasingly significant in today's environment for treating various heart diseases and presenting potential solutions. A cardiac pacemaker (CP), known as an artificial pacemaker, can treat a heart condition. Medical devices that can be implanted to regulate abnormal heart rhythms are known as artificial pacemakers. Objectives: CP needs a high level of precision and performance. It is problematic to keep CP under control. This paper goals to compare and contrast several current and cutting-edge actors-critics, policy grade soft computing (SC) techniques for unremitting control of the artificial CPs, both on and off-policy. Methods: The proportional-integral-derivative (PID) controller can adapt to the problem domain using SC approaches named SC-PID. Finding the correct balance between the exploration and exploitation stages is also critical. SC-PID becomes more influential, trustworthy, and efficient for pacemaker control. Results: Compared to the traditional PID controller techniques, SC-PID delivers a new appropriate controller approach for calculating heart rate (HR) to create a tighter match between real HR and a target profile. The above abilities make SC approaches more suitable for health care. With the introduction of low-cost, high concert digital CPUs and lower-cost memory chips, SC techniques, and their application areas will undoubtedly continue to expand. This study showed that the SC-PID controller is effective for heartbeat recovery. Conclusions: Results in this paper show that the SC-PID controller will be more helpful not only for the treatment of heart disease but also for improving the performance of implantable pacemakers in contrast to conventional PID controllers. This study underscores the potential of soft computing as a supportive tool in health care, aligning with Sustainable Development Goal (SDG) 3’s vision of good health and well-being for all.

I: General Issues.- Modeling Knowledge: Model-based Decision Support and Soft Computations.- Benefits of Decision Support Using Soft Computing.- Evolving Connectionist-based Decision Support Systems.- An Agent-based Soft Computing Society with Application Applications in Financial Investment Planning.- A Rough Sets/Neoral Networks Approach to Knowledge Discovery for the Development of Decision Support Systems.- II: Applications and Implementations.- Decision Support Systems in Healthcare: Emerging Trends and Success Factors.- A View of Public and Private Sectors for Taiwan's BOT Project Financing Using Fuzzy Multi-Criteria Methods.- Relational Structures for the Analysis of Decision Information in an Electronic Market.- A Fuzzy Evaluation Model: A Case for Intermodal Terminals in Europe.- Application of Kemeny's Median for Group Decision Support.- An Internet-based Group Decision and Consensus Reaching Support System.- Limpio: A DSS for the Urban Waste Collection Problem.- A Decision Support System for Air Quality Control Based on Soft Computing Methods.- Multicriteria Genetic Tuning for the Optimization and Control of HVAC Systems.- Intelligent Information Systems for Crime Analysis.- Application of Fuzzy Decision Trees to Reservoir Recognition.- Introducing SACRA: A Decision Support System for the Construction of Cattle Diets.- Prediction of Parthenium Weed Dispersal Using Fuzzy Logic on GIS Spatial Image.

Soft Computing Approaches to Fault Diagnosis for Dynamic Systems: A Survey

Software project development is very crucial, and measuring the exact cost and effort of development is becoming tedious and challenging. Organizations are trying to wind up their project of software development within the agreed budget and schedule successfully. Traditional practices are inadequate to achieve the current needs of the software industry. Underestimation and overestimation of software development effort lead to financial implications in the form of resources, cost of staffing, and budget of developing the software project. Soft computing (SC) approaches and tools deliver an addition of techniques for anticipating resistance to the deception, defect, incomplete truth for traceability and ambiguity, low arrangement cost, and strength. A large amount of SC approaches is prevailing in the literature to accomplish way-out to difficulties precisely, practically, and speedily. The approaches of SC can give better prediction, high performance, and dynamic behavior. SC deals with computational intelligence which integrates the concept of agent paradigm and SC. The proposed study presents a systematic literature review (SLR) of the approaches, tools, and techniques of SC used in the literature. The study presented a comprehensive review by searching the defined keywords in the popular libraries, filtered the paper, and obtained most relevant papers. After the selection of the papers, the quality assessment process of the included papers has been done in order to determine the relevancy of the papers. The study will help researchers in the area of research to devise novel ideas and solutions to overcome the existing issue on the basis of this study as evidence of the literature.

Intelligent Engineering Systems

Space operation tasks can be significantly improved and optimised if they are provided with valuable supporting tools such as diagnostic systems, expert sys tems, smart monitoring and alarming tools or data-driven forecasting systems. This work describes three projects where soft computing techniques such as genetic algorithms, fuzzy logic and artificial neural networks have been used to provide the desired features. The obtained results are very promising and therefore the use of soft computing techniques is highly recommended for future projects. INTRODUCTION Some space operations tasks require advanced features that are difficult to provide using traditional methodologies. These advance methodologies include among others: • Find feasible solutions of a highly constrained problem. Example: unload bias manoeuvre in three-wheels stabilized spacecraft with many attitude changes. It is difficult to find a solution that does not violate the saturation, stiction, stability or feasibility constraints. • Optimisation techniques can help to find a good solution in fixed time. Example: optimise the fuel spent in the unload bias manoeuvre taking into account the next orbit plans and constraints before the next orbit starts. • Monitoring: a method that combines several telemetry parameters and provides gradual alarms is very convenient as decision support tool. Example: monitor the spacecraft’s gyroscope fault detection; once a problem is detected a gradual alarm is fired. The engineers have time to react. • Diagnosis: the diagnosis goes beyond the monitoring. It consists of knowing the cause of an unexpected behaviour. Example: explain why an alarm has been triggered. • Data-Driven Modelling: it only takes into account the real behaviour of the spacecraft in space. By this way assumptions that are usually done when building physical models are avoided. The resulting model can be used to apply “what-if” analysis. Example: build a virtual thermal sensor to recover a failed one using only spacecraft data. SOFT COMPUTING TECHNIQUES Soft computing techniques encompass: • Genetic Algorithms: robust search algorithms that do not require knowledge of the objective function to be optimised and can search through large spaces quickly. They were derived from processes of molecular biology and the evolution of life. Their operators – crossover, mutation, and reproduction – match the synonymous biological processes. Instead of DNA, they usually process strings of symbols of finite length; these symbols encode the parameters to be optimised. • Fuzzy Logic: it provides an approach to approximate reasoning in which the rules of inference are approximate rather than exact. Fuzzy logic is useful in manipulating information that is incomplete, imprecise, or unreliable. Also called fuzzy set theory, fuzzy logic extends the simple Boolean operators, can express implication, and is used extensively in Artificial Intelligence (AI) programs. • Artificial Neural Networks: are parallel computation models made of adaptative processing units called neurons. These networks are parallel implementations of non-linear systems. Their major characteristic is the ability to learn by examples. This ability is very useful when we do not know how a complex system works but we have some data (examples) of its behaviour. Another interesting characteristic is its intrinsic parallelism that allows obtaining solutions very fast when implemented in hardware.

Image segmentation, the splitting of a multispectral and panchromatic image into groups of homogeneous pixels based on the region of interest(ROI), is a universal step for many advanced image processing and object recognition. Image segmentation essentially affects the overall performance of any automated image analysis system due to utmost importance of its quality. Image segmentation can be performed by recursively splitting the whole image or by merging together a large number of minute regions until a specified condition is satisfied. Thresholding is an old, simple and important method in gray scale image segmentation. In this paper, we have used Shannon’s entropy and proposed a new multilevel thresholding image segmentation method based on minimization of bi-entropy function. A smoothing technique based on weight value of the pixel within a w × w moving window is introduced to make the splitting result continuous and qualitative. The proposed algorithm takes full account of the spatial information and the gray information to decrease the computing quantity. Standard medical images, texture images, and remote sensing images are segmented in the experiment and compared with other related segmentation methods with different measures. Experimental results show that the proposed method can quickly converge with high computational efficiency.

Given the changing nature of power systems, it is challenging to optimize the controller for controlling load frequency problems. Distributed power generating sources and power system reorganization with multi-sources and multi-stakeholders make traditional load frequency control approaches unsuitable for current power systems. This research provides the comparative analysis of regulation of the load frequency in a multiple-area deregulated electricity system with the help of soft computing. In a reorganized electrical system, the major objectives of load frequency control (LFC) are to set up system frequency into acceptable limit, swiftly return the frequency to the setpoint, reduce tie-line power flow fluctuations across adjacent control zones, and track load demand agreements. To achieve LFC's goals, proportional integral derivative (PID) gain values must be tuned, for optimization purpose, soft computational methods are used in this present work. MATLAB/Simulink simulation results show that soft computing controllers can keep tie line power interchange within contracted constraints and frequency variation within the allowed range. This article compares auto tuned PID, genetic algorithm (GA), and particle swarm optimization (PSO) controllers in unregulated circumstances, load frequency regulation of two-area power systems.

We describe in this book, new methods for building intelligent systems using type-2 fuzzy logic and soft computing techniques. Soft Computing (SC) consists of several computing paradigms, including type-1 fuzzy logic, neural networks, and genetic algorithms, which can be used to create powerful hybrid intelligent systems. In this book, we are extending the use of fuzzy logic to a higher order, which is called type-2 fuzzy logic. Combining type-2 fuzzy logic with traditional SC techniques, we can build powerful hybrid intelligent systems that can use the advantages that each technique offers. We consider in this book the use of type-2 fuzzy logic and traditional SC techniques to solve pattern recognition problems in real-world applications. We consider in particular the problems of face, fingerprint and voice recognition. We also consider the problem of recognizing a person by integrating the information given by the face, fingerprint and voice of the person. Other types of applications solved with type-2 fuzzy logic and SC techniques, include intelligent control, intelligent manufacturing, and adaptive noise cancellation.

The vehicular ad hoc network is an emerging area of technology that provides intelligent transportation systems with vast advantages and applications. Frequent disconnections between the vehicular nodes due to high-velocity vehicles impact network performance. This can be addressed by efficient clustering techniques. Several recent studies have attempted to develop optimal clustering algorithms to improve network performance metrics using soft computing techniques. Although sufficient work on soft computing techniques has been carried out, it seems less commonplace to find an analysis of various algorithms’ network parameters together. This paper provides a systematic analysis of the clustering-based routing protocols used in vehicular networks that are aware of soft computing techniques. The categorization is performed according to various soft computing techniques: particle swarm optimization, k-means, neural networks, artificial bee colony, genetic algorithm, firefly algorithm, and fuzzy logic. A comparative study of soft computing strategies is also provided in the survey with a focus on their objectives, along with their strengths and limitations. This survey makes it easier for researchers to pick the required soft computing technique used in vehicular networks in order to improve metrics such as packet delivery ratio, end-to-end delay, throughput, cluster lifetime, and message overhead.

Cement is among the major contributors to the global carbon dioxide emissions. Thus, sustainable alternatives to the conventional cement are essential for producing greener concrete structures. Rice husk ash has shown promising characteristics to be a sustainable option for further research and investigation. Since the experimental work required for assessing its properties is both time consuming and complex, machine learning can be used to successfully predict the properties of concrete containing rice husk ash. A total of 192 data points are used in this study to assess the compressive strength of rice husk ash blended concrete. Input parameters include age, amount of cement, rice husk ash, super plasticizer, water, and aggregates. Four soft computing and machine learning methods, i.e., artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), multiple nonlinear regression (NLR), and linear regression are employed in this research. Sensitivity analysis, parametric analysis, and correlation factor (R2) are used to evaluate the obtained results. The ANN and ANFIS outperformed other methods.

Predicting elastic modulus degradation of alkali silica reaction affected concrete using soft computing techniques: A comparative study

Novel applications of artificial intelligence for tuning the parameters of industrial machines for optimal performance are emerging at a fast pace. Tuning the combine harvesters and improving the machine performance can dramatically minimize the wastes during harvesting, and it is also beneficial to machine maintenance. Literature includes several soft computing, machine learning and optimization methods that had been used to model the function of harvesters of various crops. Due to the complexity of the problem, machine learning methods had been recently proposed to predict the optimal performance with promising results. In this paper, through proposing a novel hybrid machine learning model based on artificial neural networks integrated with particle swarm optimization (ANN- PSO), the performance analysis of a common combine harvester is presented. The hybridization of machine learning methods with soft computing techniques has recently shown promising results to improve the performance of the combine harvesters. This research aims at improving the results further by providing more stable models with higher accuracy.

Due to the growing variety and quantity of information available on the Web, there is urgent need for developing Web-based applications capable of adapting their services to the needs of the users. This is the main rationale behind the flourishing area of Web personalization that finds in soft computing (SC) techniques a valid tool to handle uncertainty in Web usage data and develop Web-based applications tailored to user preferences. The main reason for this success seems to be the synergy resulting from SC paradigms, such as fuzzy logic, neural networks, and genetic algorithms. Each of these computing paradigms provides complementary reasoning and searching methods that allow the use of domain knowledge and empirical data to solve complex problems. In this chapter, we emphasize the suitability of hybrid schemes combining different SC techniques for the development of effective Web personalization systems. In particular, we present a neuro-fuzzy approach for Web personalization that combines techniques from the fuzzy and the neural paradigms to derive knowledge from Web usage data and represent the knowledge in the comprehensible form of fuzzy rules. The derived knowledge is ultimately used to dynamically suggest interesting links to the user of a Web site.