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Nature Inspired Computing (NIC) paradigms, namely, swarm intelligence, evolutionary computation and computational intelligence techniques are widely applied to develop versatile and adaptable systems and create computational methods that can assist human to resolve real world complex problems. It can be achieved by transferring knowledge from natural systems to engineering systems. The objective of this paper is to analyze the recent trends and advancement in the application of metaheuristic algorithms to solve various domains, especially, denoising, edge detection and classification problem. This paper provides a comprehensive list of global optimization algorithms that can be applied to develop innovative system and soft computing applications integrates with computational intelligence techniques. Several fitness functions and parameters implemented in the evaluation of global optimization algorithm to find global optimum for solving diverse applications are thoroughly investigated. The implications for the selection of fitness function to solve specific optimization problems and applications are also discussed.

This study discusses the Islamic digital network (DIN) innovation for banking service products in the digitalization era at Bank Muamalat KCP Kab.Bone. The objectives of this research are (1). To find out the muamalat digital Islamic network (DIN) innovation for banking services. (2). To find out the impact of the muamalat digital Islamic network (DIN) innovation on the optimization of banking services in the digitalization era. The type of this research is the type of field research (field research) with the research approach used is a qualitative approach. This technique is used to describe the data that researchers collect, both data from interviews, observations and documentation during research at Bank Muamalat KCP Kab. Bones. The results of this study indicate that (1).Muamalat digital Islamic network (DIN) is a mobile banking muamalat which is then transformed into a digital Islamic network muamalat. In the application of the muamalat digital Islamic network (DIN) which was introduced and implemented throughout Indonesia on November 14, 2019, Bank Muamalat targets to provide convenient and uncomplicated services so that it has positive implications for customers. This product is upgraded so that it is not inferior to other products and so that it can compete in the digital 4.0 era (2). The impact of the implementation of the Islamic digital Islamic network (DIN) on the optimization of banking services in the digitalization era are: the use of the digital Islamic network (DIN) muamalat provides easy service to customers, saves time, provide security to customers and provide satisfaction to customers. Thus the creation of effectiveness and efficiency in the application of digital Islamic network (DIN) muamalat, there will be optimization of banking services in the digital era.

Micro combined heat and power home supply: Prospective and adaptive management achieved by computational intelligence techniques

The aim of this study was to estimate genetic divergence and select BC1F3 populations of dwarf tomato plant within the Santa Cruz segment by computational intelligence techniques. The experiment was conducted in a greenhouse in the Vegetable Crop Experimental Station of the Universidade Federal de Uberlândia (UFU), Monte Carmelo, MG, Brazil. A randomized block experimental design was used with 17 treatments and four replications. The genetic material evaluated comprised thirteen dwarf tomato plant populations obtained by a backcross and two self-fertilizations, plus both parents (recurrent and donor), and two commercial check varieties. The traits evaluated were mean fruit weight (MFW), soluble solids content (SSC), fruit diameter (FD), fruit length (FL), fruit shape (FS), pulp thickness (PT), number of locules (NL), distance between internodes, and acylsugar, β-carotene, and lycopene content. The data were analyzed by means testing, and genetic divergence was measured using Mahalanobis generalized distance by the unweighted pair group method with arithmetic mean (UPGMA) and through computational intelligence using Kohonen self-organizing maps (SOM). Genetic dissimilarity in relation to the donor parent could be confirmed through both methodologies. However, the SOM was able to detect differences and organize the similarities among the populations in a more consistent manner, resulting in a larger number of groups. In addition, the computational intelligence techniques allow the weight of each variable in formation of the groups to be ascertained. Among the BC1F3 populations, UFU-SC#3 and UFU-SC#5 stood out for agronomic traits, and UFU-SC#10 and UFU-SC#11 stood out for quality parameters.

Computational intelligence techniques have been successfully used for solving control problems in packet-switching network architectures. The introduction of active networking adds a high degree of flexibility in customizing the network infrastructure and introducing new functionality. Therefore, there is a clear need for investigating both the applicability of computational intelligence techniques in this new networking environment, as well as the provisions of active networking technology that computational intelligence techniques can exploit for improved operation. We report on the characteristics of these technologies, their synergy and on outline recent efforts in the design of a computational intelligence toolkit and its application to routing on a novel active networking environment.

In the realm of computational intelligence, various nature-inspired computational methodologies are being used. To solve real-world problems, artificial neural networks (ANNs), swarm intelligence, and fuzzy systems are used, to name a few. In current time, we are surrounded by sensors, various machines, and electronic devices. All of these generate large quantities of data. This large volume of data has changed our lives in a variety of ways, and the field of healthcare is no exception. Clinical trials, personalized gadgets like smart watches, fit-bits, medical insurance records, hospital billing software, medical reports, genetic information, medical IOT devices, medical imaging, scholarly journals, pathology lab networks, and other sources of data generate massive amounts of information in healthcare. The amount of data being generated is increasing all the time. Recent improvements in computational intelligence techniques have enriched the importance of big data analytics, genetics & genomics, medical image processing, computer vision, Internet of Things, drug discovery, disease diagnosis, and disease prevention in the field of healthcare. The main computational intelligence techniques to address these issues will be discussed in this chapter. Healthcare is a critical industry because, in addition to money, people's lives are at risk. As a result, the approaches and models employed must be extremely accurate and efficient. The effectiveness of a machine learning model is determined by the quality of data provided to it during the learning phase. Data errors, such as missing data and unbalanced data, are particularly widespread in the healthcare industry. In this chapter, we'll examine feature engineering as a solution to these kinds of problems. Healthcare is projected to be significantly impacted by genetics and genomics, which will disclose new dimensions, including personalised medicine, disease prediction, and genetic engineering. This case study will be presented in this chapter.

Computational intelligence is a modern research orientation to ensure effective and efficient outcomes for real world problems. As it is highly associated with essentials of statistics, data analytics, and artificial intelligence, technical fields have been intensively focused on using specific techniques to solve different problems. However, predictive analysis has been a modern solution approach for different aspects of advanced problems. Engineering-related applications find computational intelligence techniques useful to have desired outcomes at the end. Among the associated technical issues, Concrete technology finds its way by raising different issues and combining already known methods with the support of computational intelligence. This chapter provides a brief review of computational intelligence techniques to be used in concrete technology. The introduction is related to applications of predicting compressive strength and the ultrasonic pulse velocity. In this chapter, an overview of this book is provided. As an editorial introduction, remarkable original content texts by the book authors are combined in order to provide the most essential knowledge for the readers.

Optimization problems arise in various fields of science, engineering, and industry. In many occasions, such optimization problems, particularly in the present scenario, involve a variety of decision variables and complex structured objectives, and constraints. Often, the classical or traditional optimization techniques face difficulty in solving such real world optimization problems in their original form. Due to deficiencies of classical optimization algorithms in solving large-scale, highly non-linear, and often non-differentiable problems, there is a need to develop efficient and robust computational algorithms, which can solve problems, numerically irrespective of their sizes. Taking inspiration from nature to develop computationally efficient algorithms is one way to deal with real world optimization problems. Broadly, one can put these algorithms in the field of computational sciences and in particular, to computational intelligence. Formally, computational intelligence (CI) is a set of nature-inspired computational methodologies and approaches to solve complex real world problems. The major constituents of CI are Fuzzy Systems (FS), Neural Networks (NN), and Swarm Intelligence (SI) and Evolutionary Computation (EC). Computational intelligence techniques are powerful, efficient, flexible, and reliable. Swarm Intelligence and Evolutionary Computation are two very useful components of computational intelligence that are primarily used to solve optimization problems. This book primarily concerns with various swarm and evolutionary optimization algorithms. This chapter provides a brief introduction to swarm and evolutionary algorithms.

During the past, there was a massive growth of knowledge of unknown proteins with the advancement of high throughput microarray technologies. Protein function prediction is the most challenging problem in bioinformatics. In the past, the homology based approaches were used to predict the protein function, but they failed when a new protein was different from the previous one. Therefore, to alleviate the problems associated with homology based traditional approaches, numerous computational intelligence techniques have been proposed in the recent past. This paper presents a state-of-the-art comprehensive review of various computational intelligence techniques for protein function predictions using sequence, structure, protein-protein interaction network, and gene expression data used in wide areas of applications such as prediction of DNA and RNA binding sites, subcellular localization, enzyme functions, signal peptides, catalytic residues, nuclear/G-protein coupled receptors, membrane proteins, and pathway analysis from gene expression datasets. This paper also summarizes the result obtained by many researchers to solve these problems by using computational intelligence techniques with appropriate datasets to improve the prediction performance. The summary shows that ensemble classifiers and integration of multiple heterogeneous data are useful for protein function prediction.

In recent years, computer network communication technology has made considerable progress and development, and its scale has now covered all aspects of society and people’s lives. However, while computer network technology promotes social development and improves the convenience of people’s lives, there are still many security problems, especially the difficulty of ensuring the security of user data, which has become a prominent problem in the further development and improvement of communication technology. This article takes the network communication security technology as an entry point, and analyzes its technical types with the aid of data encryption technology, and then analyzes and studies its security in computer network communication data transmission according to the specific points proposed. Through the substantial technical analysis of the computer network security system, the possibility of the system being attacked can be reduced to a certain extent, and the security of network data can be improved. Through the analysis of the integrity of the network system, the overall framework of a distributed network security early warning system based on the network is established, and various structures of the system are analyzed. The network load analysis module is responsible for using key performance index data, such as user traffic, number of users and wireless utilization, and analyzes and classifies the network cell load by combining the partial least square method and BP neural network, so that the key cell performance analysis module is mainly Analyze key performance index data of key communities, obtain early warning thresholds through clustering algorithms, and implement a dynamic early warning system to provide guidance for network planning and mining in key small areas. The database module is responsible for storing all performance data and performance analysis results; the performance query module is responsible for querying and analyzing performance data and static warnings, and presents them to users in the form of text and graphics. Experimental research results show that in this information age, the development of computer networks plays a very important role in the development of our country. Computer network operators make full use of cluster analysis, data mining, encryption algorithms, and early warning systems to conduct research to fully improve the security performance of data in network communications.

The induction machine is a critical component in various industrial applications, and its reliable operation is paramount for uninterrupted processes. To ensure the continuous performance and prevent unexpected failures, the implementation of an effective health monitoring system is imperative. In this study, we propose a novel approach to monitor the health of induction machines using computational intelligence techniques. The proposed system integrates advanced computational intelligence algorithms, including machine learning and signal processing methods, to analyze the operational data and identify potential faults or anomalies in the induction machine. Specifically, we employ techniques such as artificial neural networks, support vector machines, and genetic algorithms to process the sensor data acquired from the induction machine. The health monitoring system operates in real-time, continuously analyzing various parameters such as current, voltage, temperature, and vibration patterns to detect deviations from normal operating conditions. By establishing baseline performance profiles and employing pattern recognition algorithms, the system can identify early signs of degradation or impending failures, allowing for proactive maintenance interventions. Moreover, the proposed system offers flexibility and scalability, enabling adaptation to different types of induction machines and operating environments. It facilitates remote monitoring capabilities, enabling maintenance personnel to access real-time health status and diagnostic information from anywhere, facilitating timely decision-making and proactive maintenance strategies.

Secret voting in democratic countries using computational intelligence and Blockchain Technology, the vote is a fundamental right in democracy to elect people’s representatives at various levels. Manual voting is tedious and error prone. E-voting systems replaced the manual voting systems to overcome illegal voting and malpractices. Due to growing cybercrimes, the existing e-voting systems are not ideal and face many challenges like authentication, privacy, data integrity, etc. This paper presents computational intelligence and Blockchain technology in developing secret e-voting systems. This paper addresses various issues not addressed by traditional and e-voting systems and proposes new voting using computational intelligence techniques and blockchain technology.

An Internet Java Applet http://www.cs.essex.ac.uk/staff/poli/SoloPong/ allows users anywhere to play the Solo Pong game. We compare people's performance to a hand coded optimal player and programs automatically produced by computational intelligence. The computational intelligence techniques are: genetic programming, including a hybrid of GP and a human designed algorithm, and a particle swarm optimiser. The computational intelligence approaches are not fine tuned. GP and PSO find good players. Evolutionary computation (EC) is able to beat both human designed code and human players.

Underwater wireless sensor networks (UWSNs) is emerging as an advance terminology for monitoring and controlling the underwater aquatic life. This technology determines the undiscovered resources present in the water through computational intelligence (CI) techniques. CI here pertains to the capability of a system to acquire a specific task from data or experimental surveillance below the water. In today’s time data is considered as the identity for everything that exists in nature, whether that data is related to human beings, machines or any type of device like internet of underwater things (IoUT). The collected data should be correct, complete and fulfill the requirements of a particular task to be done. Underwater data collection is very tough because of sensors mobility due to water drift 3 meters/sec, crest and trough. A lot of packet drop also exists due to underwater conditions that hurdles the data collection process. Various techniques already exists for efficient collection of data below the water but these are not properly classified. This manuscript has summarized the concept of data collection in UWSN along with its classification based on routing. Also, a short discussion about existence of CORONA below the water along with water purification is carried out. Furthermore, some data routing approaches are also analyzed on the basis of quality of service parameters and the current challenges to be tackled during data collection are also discussed.

Short-term stream flow forecasts are required for simulation, optimization, and decision-making purposes in applications ranging from hydropower planning to flood prevention.The particular case of one-day ahead stream flow forecasting is an important but difficult problem that has been increasingly studied using hybrid computational intelligence and machine learning techniques.However, these studies present several limitations.In this work we attempt to address those limitations by (1) replicating and validating previous works; (2) using more objective evaluation criteria; (3) applying several computational intelligence techniques to datasets representative of diverse geographic areas; (4) preprocessing data and performing an extensive parameter optimization in order to improve previous results.