Soft Computing Techniques Research Articles

ADVANCING INFORMATION TECHNOLOGY WITH IMMUNOLOGICAL COMPUTING – SOFT COMPUTING TECHNIQUES FOR ADAPTIVE AND ROBUST SYSTEMS

The field of Information Technology (IT) is evolving rapidly, and with this growth comes the need for systems that are both adaptive and robust. Biological systems, especially the human immune system, demonstrate remarkable adaptability and resilience, inspiring the development of Immunological Computing (IC). This paper explores the application of immunological principles in Soft Computing techniques to create systems capable of responding to dynamic environments. Current IT systems often face challenges such as handling unpredictable changes, scalability, and security threats. Traditional computing approaches struggle to address these issues efficiently due to their rigid structures and limited adaptability. Immunological Computing, inspired by the immune system’s ability to learn, remember, and adapt, offers a promising solution. The proposed method integrates immune system mechanisms like clonal selection, immune memory, and self/non-self-recognition into computational models. These models are coupled with soft computing techniques such as fuzzy logic, genetic algorithms, and neural networks, enhancing the system’s ability to adapt to changing environments and uncertainties. In simulated tests, this approach demonstrated a significant improvement in robustness and adaptability compared to traditional IT systems. For instance, in a cybersecurity application, the immunological-based system detected and neutralized 94.6% of threats, a notable improvement over the 82.3% detected by conventional systems. Similarly, in a resource optimization scenario, the system adapted to dynamic workloads with an efficiency increase of 15% compared to static systems.

Leveraging microbial synergy: Predicting the optimal consortium to enhance the performance of microbial fuel cell using Subspace-kNN

Microbial Fuel Cells (MFCs) are a sophisticated and advanced system that uses exoelectrogenic microorganisms to generate bioenergy. Predicting performance outcomes under experimental settings is challenging due to the intricate interactions that occur in mixed-species bioelectrochemical reactors like MFCs. One of the key factors that limit the MFC's performance is the presence of a microbial consortium. Traditionally, multiple microbial consortia are implemented in MFCs to determine the best consortium. This approach is laborious, inefficient, and wasteful of time and resources. The increase in the availability of soft computational techniques has allowed for the development of alternative strategies like artificial intelligence (AI) despite the fact that a direct correlation between microbial strain, microbial consortium, and MFC performance has yet to be established. In this work, a novel generic AI model based on subspace k-Nearest Neighbour (SS-kNN) is developed to identify and forecast the best microbial consortium from the constituent microbes. The SS-kNN model is trained with thirty-five different microbial consortia sharing different effluent properties. Chemical oxygen demand (COD) reduction, voltage generation, exopolysaccharide (EPS) production, and standard deviation (SD) of voltage generation are used as input features to train the SS-kNN model. The proposed SS-kNN model offers an accuracy of 100% during training period and 85.71% when it is tested with the data obtained from existing literature. The implementation of selected consortium (as predicted by SS-kNN model) improves the COD reduction capability of MFC by 15.67% than that of its constituent microbes which is experimentally verified. In addition, to prevent the effects of climate change and mitigate water pollution, the implementation of MFC technology ensures clean and green electricity. Consequently, achieving sustainable development goals (SDG) 6, 7, and 13.

Reliability‐based state parameter liquefaction probability prediction using soft computing techniques

The state parameter (ѱ) accounts for both relative density and effective stress, which influence the cyclic stress or liquefaction characteristic of the soil significantly. This study presents a ѱ‐based probabilistic liquefaction evaluation method using six soft computing (SC) techniques. The liquefaction probability of failure (PL) is calculated using the first‐order second moment (FOSM) method based on the cone penetration test (CPT) database. Then, six SC techniques, such as Gaussian process regression (GPR), relevance vector machine (RVM), functional network (FN), genetic programming (GP), minimax probability machine regression (MPMR) and multivariate adaptive regression splines (MARS), are used to predict PL. The performance of these models is examined using nine statistical indices. Additionally, plots such as regression plots, Taylor diagrams, error matrix and rank analysis are shown to assess the SC model's performance. Finally, sensitivity analysis is performed using the cosine amplitude method (CAM) to assess the influence of input parameters on output. The current study demonstrates that SC models based on state parameter predict PL effectively. RVM and MPMR models closely follow the GPR model in terms of performance, which is superior to the other models. Notably, two equations are generated using GP and MARS models to predict PL. The results of the sensitivity analysis reveal the magnitude of earthquake (Mw) as the most sensitive parameter. The outcomes of this research will offer risk evaluations for geotechnical engineering designs and expand the use of state parameter‐based SC models in liquefaction analysis.

Exploratory techniques to analyse Ecuador's tourism industry

Abstract The analysis of the operation of tourism companies will provide valid information for the design of policies to reactivate the tourism industry, which has been strongly affected during the pandemic generated by COVID-19. The objective of this paper is to use soft computing techniques to analyse tourism companies in Ecuador. First of all, dimensionality reduction methods are applied: principal component analysis, isometric feature mapping and locally linear embedding, on data of tourism enterprises in Ecuador for the year 2015. In addition, to verify the trend of operational variables, the data of tourism companies in Ecuador in 2019 and 2020 are analysed with dimensionality reduction methods that improve the interpretation by minimizing the loss of information. The data sets are analysed with k-means, k-medoids and Hierarchical Clustering, generating groups according to similar characteristics. The optimal number of clusters is determined with the following: Elbow Method, Silhouette Coefficient, Davies-Bouldin Index and Dunn Index. In addition, an analysis of the operation of tourism companies in the year 2020 concerning previous years is included. The study allows exploring Soft Computing techniques to identify important information for the definition of strategies that contribute to an effective reactivation of the tourist industry of Ecuador.

Result and Analysis of Soft Computing Techniques for Diagnosis of Skin Diseases

Abstract: A prevalent bodily issue that has a significant impact on people's lives is skin disease. An early and precise identification of the condition can assist patients in receiving proper care, hastening their recovery. Convolutional neural networks (CNN) powered by deep learning have recently made major advancements that have greatly increased the accuracy of illness categorization. This led to the motivation for this work, which used deep CNN architectures to identify two different skin conditions—eczema and psoriasis. Ten fold cross validation has been utilized to examine the performance of five distinct cutting-edge CNN architectures. With the Adam optimizer, the Inception ResNet v2 architecture achieves a maximum validation accuracy of 97.1%. The results of the performance matrices suggest that the model performs remarkably well in terms of diagnosing skin disorders. Additionally, the study demonstrates two approaches for the practical application of the implemented model. (i) CNN Approach ii) PCA Approach.

Review of Underwater Wireless Sensor Networks Deployment Techniques and Challenges

Underwater Wireless Sensor Networks (UWSNs) have become increasingly important due to their critical roles in marine life monitoring, communication, ocean data collection, sampling, and military security operations. The success of UWSNs largely depends on efficient node deployment techniques that ensure optimal coverage, connectivity, cost-effectiveness, network lifetime, and energy utilization. This paper presents a comprehensive review of various node deployment types and techniques specifically designed for UWSNs. It covers depth-adjustment, movement-assisted, self-movement, and soft-computing techniques, highlighting their advantages, limitations, and application scenarios. Each technique is evaluated based on key performance metrics such as network coverage, connectivity, energy consumption, network lifetime, and deployment cost. Additionally, the paper discusses the challenges and identifies open research directions in the field, providing valuable insights for researchers and practitioners in selecting appropriate node deployment techniques for UWSNs.

Sustainable Soft Computing Techniques for Site Selection of Optimal Renewable Energy Source to Reduce Carbon Emission in India - A Mini Review

Sustainable Soft Computing Techniques for Site Selection of Optimal Renewable Energy Source to Reduce Carbon Emission in India - A Mini Review

UAV path planning techniques: a survey

Unmanned Aerial Vehicles (UAVs) are ideally suited for many real-world applications ranging from scientific to commercial, industrial, and military fields. Enhancing the efficiency of UAV-based missions through optimization techniques is of paramount significance. In this regard, the path planning problem that refers to finding the best collision-free path between the start point and the destination by addressing temporal, physical, and geometric constraints is a key issue. In this paper, a review of recent path planning methods from different perspectives with a clear and comprehensive categorization is presented. This study provides a general taxonomy categorizing the existing works into classical approaches, soft-computing techniques, and hybrid methods. Here, a detailed analysis of the recent techniques as well as their advantages and limitations is offered. Additionally, it provides an overview of environment modeling methods, path structures, optimality criteria, completeness criteria, and current UAV simulators.

Analysis of Concrete Compression Test by Soft Computing Techniques

Analysis of Concrete Compression Test by Soft Computing Techniques

Prediction of rock fragmentation in a fiery seam of an open-pit coal mine in India

Spontaneous combustion of coal increases the temperature in adjoining overburden strata of coal seams and poses a challenge when loading blastholes. This condition, known as hot-hole blasting, is dangerous due to the increased possibility of premature explosions in loaded blastholes. Thus, it is crucial to load the blastholes with an appropriate amount of explosives within a short period to avoid premature detonation caused by high temperatures of blastholes. Additionally, it will help achieve the desired fragment size. This study tried to ascertain the most influencial variables of mean fragment size and their optimum values adopted for blasting in a fiery seam. Data on blast design, rock mass, and fragmentation of 100 blasts in fiery seams of a coal mine were collected and used to develop mean fragmentation prediction models using soft computational techniques. The coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), mean square error (MSE), variance account for (VAF) and coefficient of efficiency in percentage (CE) were calculated to validate the results. It indicates that the random forest algorithm (RFA) outperforms the artificial neural network (ANN), response surface method (RSM), and decision tree (DT). The values of R2, RMSE, MAE, MSE, VAF, and CE for RFA are 0.94, 0.034, 0.027, 0.001, 93.58, and 93.01, respectively. Multiple parametric sensitivity analyses (MPSAs) of the input variables showed that the Schmidt hammer rebound number and spacing-to-burden ratio are the most influencial variables for the blast fragment size. The analysis was finally used to define the best blast design variables to achieve optimum fragment size from blasting. The optimum factor values for RFA of S/B, ld/B and ls/ld are 1.03, 1.85 and 0.7, respectively.

Soft Computing Technique for Harmonic Mitigation of Enhancing Grid-Integrated Photovoltaic (PV) System Power Quality

This abstract explores the use of soft computing techniques for harmonic mitigation in renewable energy integration, particularly for photovoltaic (PV) systems in power grids. Traditional methods often fail to effectively mitigate harmonics, leading to system performance compromise and grid instability. Soft computing's adaptability and ability to handle complex systems offer promising solutions. The study explores the use of soft computing techniques like artificial neural networks, fuzzy logic, and genetic algorithms to mitigate harmonics in PV systems. ANNs learn complex patterns, fuzzy logic handles uncertain data, and genetic algorithms optimize mitigation strategies for varying grid conditions and system dynamics. The study demonstrates the effectiveness of soft computing-based harmonic mitigation techniques in improving power quality indices like total harmonic distortion and voltage stability. It emphasizes the importance of integrating advanced computational approaches in PV systems for seamless grid integration and promoting sustainable renewable energy integration. Key Word: Renewable Energy sources, PV systems, harmonics, fuzzy logic controller, ANNs

Evaluation of soft-computing techniques for pan evaporation estimation

Estimation of pan evaporation (Epan) can be useful in judicious irrigation scheduling for enhancing agricultural water productivity. The aim of present study was to assess the efficacy of state-of-the-art LSTM and ANN for daily Epan estimation using meteorological data. Besides this, the effect of static time-series (Julian date) as additional input variable was investigated on performance of soft-computing techniques. For this purpose,the models were trained, tested and validated with eight meteorological variables of 37 years by using preceding 1-, 3- and 5- days’ information. Data were partitioned into three groups as training (60%), testing (20%), and validation (20%) components. It was observed that the models performed well (best) with preceding 5-days meteorological information followed by 3-days and 1-day. However, all LSTMs simulated peak value of Epan was more accurate as compared to lower values. Meteorological data with julian date improved the performance of LSTMs (0.75<NSE 1; PBias< 10; KGE 0.75). The ANN trained using only meteorological data (preceding 5-days information) had better performance error statistics among all other ANNs and LSTMs with minimum MAE (0.68 to 0.86), RMSE (0.93 to 1.22), PBias (-0.73 to 2.44) and maximum NSE (0.83 to 0.84) and KGE (0.89 to 0.92). Overall, it was inferred that the forecasting of meteorological parameters using a few days preceding information along with Julian date as the time series variables resulted in better estimation of Epan for the study region.

Suspended Load Modeling of River Using Soft Computing Techniques

Suspended Load Modeling of River Using Soft Computing Techniques

Benchmarking of Various Flexible Soft-Computing Strategies for the Accurate Estimation of Wind Turbine Output Power

This computational study explores the potential of several soft-computing techniques for wind turbine (WT) output power (kW) estimation based on seven input variables of wind speed (m/s), wind direction (°), air temperature (°C), pitch angle (°), generator temperature (°C), rotating speed of the generator (rpm), and voltage of the network (V). In the present analysis, a nonlinear regression-based model (NRM), three decision tree-based methods (random forest (RF), random tree (RT), and reduced error pruning tree (REPT) models), and multilayer perceptron-based soft-computing approach (artificial neural network (ANN) model) were simultaneously implemented for the first time in the prediction of WT output power (WTOP). To identify the top-performing soft computing technique, the applied models’ predictive success was compared using over 30 distinct statistical goodness-of-fit parameters. The performance assessment indices corroborated the superiority of the RF-based model over other data-intelligent models in predicting WTOP. It was seen from the results that the proposed RF-based model obtained the narrowest uncertainty bands and the lowest quantities of increased uncertainty values across all sets. Although the determination coefficient values of all competitive decision tree-based models were satisfactory, the lower percentile deviations and higher overall accuracy score of the RF-based model indicated its superior performance and higher accuracy over other competitive approaches. The generator’s rotational speed was shown to be the most useful parameter for RF-based model prediction of WTOP, according to a sensitivity study. This study highlighted the significance and capability of the implemented soft-computing strategy for better management and reliable operation of wind farms in wind energy forecasting.

Application of Advanced Soft Computing Techniques for Power Quality Improvement Using Active Power Filters in Radial Distribution System

This paper investigates the application of advanced soft computing techniques: northern goshawk optimization (NGO) and coati optimization algorithm (COA) for improving power quality (PQ) using active power filter (APF) in radial distribution systems (RDS). While beneficial, the growing adoption of solar photovoltaic (SPV) systems introduces power quality concerns like harmonic distortion due to their nonlinear nature. This distribution generator (DG) type is called nonlinear DG (NLDG). Harmonics is the leading cause of poor PQ. This study considers nonlinear loads (NLs) at two end nodes and nonlinear DG (NLDG) integrated into the RDS. APFs are optimally placed to reduce the harmonics and enhance PQ. The proposed approach utilizes soft computing techniques to minimize the APF current while adhering to inequality constraints. The NGO, inspired by natural processes, is employed for optimal APF sizing. This method prioritizes a balance between exploration and exploitation for efficient searching. The effectiveness of NGO is evaluated through simulations on the IEEE-69 bus RDS and compared with another recent soft computing technique, the COA. Here, four different cases are considered: a) only NL+NLDG, b) APF at bus 27, c) APF at bus 35, and d) APFs are at 27 and 35 buses. These cases are considered to analyze the effect of placement and sizing of APFs on PQ in RDS. The results validate the stability and efficacy of NGO in addressing this optimization problem for PQ improvement in RDS.

Comparison of soft-computing techniques: Data-driven models for flood forecasting

<p>Accurate flood forecasting is a crucial process for predicting the timing, occurrence, duration, and magnitude of floods in specific zones. This prediction often involves analyzing various hydrological, meteorological, and environmental parameters. In recent years, several soft computing techniques have been widely used for flood forecasting. In this study, flood forecasting for the Narmada River at the Hoshangabad gauging site in Madhya Pradesh, India, was conducted using an Artificial Neural Network (ANN) model, a Fuzzy Logic (FL) model, and an Adaptive Neuro-Fuzzy Inference System (ANFIS) model. To assess their capacity to handle different levels of information, three separate input data sets were used. Our objective was to compare the performance and evaluate the suitability of soft computing data-driven models for flood forecasting. For the development of these models, monthly discharge data spanning 33 years from six gauging sites were selected. Various performance measures, such as regression, root mean square error (RMSE), and percentage deviation, were used to compare and evaluate the performances of the different models. The results indicated that the ANN and ANFIS models performed similarly in some cases. However, the ANFIS model generally predicted much better than the ANN model in most cases. The ANFIS model, developed using the hybrid method, delivered the best performance with an RMSE of 211.97 and a coefficient of regression of 0.96, demonstrating the potential of using these models for flood forecasting. This research highlighted the effectiveness of soft computing techniques in flood forecasting and established useful suitability criteria that can be employed by flood control departments in various countries, regions, and states for accurate flood prognosis.</p>

Performance and Analysis of Soft Computing Techniques with Energy Management Framework in IoT Networks

Performance and Analysis of Soft Computing Techniques with Energy Management Framework in IoT Networks

Systematic Exploration Using Intelligent Computing Techniques for Clinical Diagnosis of Gastrointestinal Disorder: A Review

In today's era, the growing ratio of Gastrointestinal (GI) diseases in human beings has become a crucial point of notice and must be diagnosed as early as possible. There are various methods to diagnose abdomen-related problems using medical imaging techniques like ultrasound, endoscopy, Colonoscopy, abdominal CT scan and digital X-ray, etc. Endoscopy is one of the most efficient medical imaging techniques for diagnosing gastrointestinal (GI) diseases. Manual diagnosis of endoscopic images may have a possibility of committing mistakes in properly detecting gastrointestinal disorders because tiny particles are involved in endoscopic images and may be responsible for critical disorders. However, manual diagnosis may ignore such information because of less efficiency of vision and observation. To avoid such problems, various models based on soft computing and neuro-fuzzy techniques have been proposed to detect and classify various gastrointestinal disorders. In this article, the authors propose a systematic review of previous research that has been carried out using intelligent computing methods. Here, various conventional approaches are discussed and compared. This review research shows performance limitations due to complex data models, heterogeneous datasets and the absence of intelligent feature selection methods in diagnosing gastrointestinal disorders.

Toward empirical correlations for estimating the specific heat capacity of nanofluids utilizing GRG, GP, GEP, and GMDH

When nanoparticles are dispersed and stabilized in a base-fluid, the resulting nanofluid undergoes considerable changes in its thermophysical properties, which can have a substantial influence on the performance of nanofluid-flow systems. With such necessity and importance, developing a set of mathematical correlations to identify these properties in various conditions can greatly eliminate costly and time-consuming experimental tests. Hence, the current study aims to develop innovative correlations for estimating the specific heat capacity of mono-nanofluids. The accurate estimation of this crucial property can result in the development of more efficient and effective thermal systems, such as heat exchangers, solar collectors, microchannel cooling systems, etc. In this regard, four powerful soft-computing techniques were considered, including Generalized Reduced Gradient (GRG), Genetic Programming (GP), Gene Expression Programming (GEP), and Group Method of Data Handling (GMDH). These techniques were implemented on 2084 experimental data-points, corresponding to ten different kinds of nanoparticles and six different kinds of base-fluids, collected from previous research sources. Eventually, four distinct correlations with high accuracy were provided, and their outputs were compared to three correlations that had previously been published by other researchers. These novel correlations are applicable to various oxide-based mono-nanofluids for a broad range of independent variable values. The superiority of newly developed correlations was proven through various statistical and graphical error analyses. The GMDH-based correlation revealed the best performance with an Average Absolute Percent Relative Error (AAPRE) of 2.4163% and a Coefficient of Determination (R2) of 0.9743. At last, a leverage statistical approach was employed to identify the GMDH technique’s application domain and outlier data, and also, a sensitivity analysis was carried out to clarify the degree of dependence between input and output variables.

Prediction of Uniaxial Compressive Strength of Rocks from Their Physical Properties Using Soft Computing Techniques

Prediction of Uniaxial Compressive Strength of Rocks from Their Physical Properties Using Soft Computing Techniques