Multi-population Black Hole Algorithm for the problem of data clustering.

The retrieval of important information from a dataset requires applying a special data mining technique known as data clustering (DC). DC classifies similar objects into a groups of similar characteristics. Clustering involves grouping the data around k-cluster centres that typically are selected randomly. Recently, the issues behind DC have called for a search for an alternative solution. Recently, a nature-based optimization algorithm named Black Hole Algorithm (BHA) was developed to address the several well-known optimization problems. The BHA is a metaheuristic (population-based) that mimics the event around the natural phenomena of black holes, whereby an individual star represents the potential solutions revolving around the solution space. The original BHA algorithm showed better performance compared to other algorithms when applied to a benchmark dataset, despite its poor exploration capability. Hence, this paper presents a multi-population version of BHA as a generalization of the BHA called MBHA wherein the performance of the algorithm is not dependent on the best-found solution but a set of generated best solutions. The method formulated was subjected to testing using a set of nine widespread and popular benchmark test functions. The ensuing experimental outcomes indicated the highly precise results generated by the method compared to BHA and comparable algorithms in the study, as well as excellent robustness. Furthermore, the proposed MBHA achieved a high rate of convergence on six real datasets (collected from the UCL machine learning lab), making it suitable for DC problems. Lastly, the evaluations conclusively indicated the appropriateness of the proposed algorithm to resolve DC issues.

Insights into the photovoltaic properties of indium sulfide as an electron transport material in perovskite solar cells.

According to recent reports, planar structure-based organometallic perovskite solar cells (OPSCs) have achieved remarkable power conversion efficiency (PCE), making them very competitive with the more traditional silicon photovoltaics. A complete understanding of OPSCs and their individual parts is still necessary for further enhancement in PCE. In this work, indium sulfide (In2S3)-based planar heterojunction OPSCs were proposed and simulated with the SCAPS (a Solar Cell Capacitance Simulator)-1D programme. Initially, OPSC performance was calibrated with the experimentally fabricated architecture (FTO/In2S3/MAPbI3/Spiro-OMeTAD/Au) to evaluate the optimum parameters of each layer. The numerical calculations showed a significant dependence of PCE on the thickness and defect density of the MAPbI3 absorber material. The results showed that as the perovskite layer thickness increased, the PCE improved gradually but subsequently reached a maximum at thicknesses greater than 500 nm. Moreover, parameters involving the series resistance as well as the shunt resistance were recognized to affect the performance of the OPSC. Most importantly, a champion PCE of over 20% was yielded under the optimistic simulation conditions. Overall, the OPSC performed better between 20 and 30 °C, and its efficiency rapidly decreases above that temperature.

Exploiting the Generative Adversarial Network Approach to Create a Synthetic Topography Corneal Image.

Corneal diseases are the most common eye disorders. Deep learning techniques are used to perform automated diagnoses of cornea. Deep learning networks require large-scale annotated datasets, which is conceded as a weakness of deep learning. In this work, a method for synthesizing medical images using conditional generative adversarial networks (CGANs), is presented. It also illustrates how produced medical images may be utilized to enrich medical data, improve clinical decisions, and boost the performance of the conventional neural network (CNN) for medical image diagnosis. The study includes using corneal topography captured using a Pentacam device from patients with corneal diseases. The dataset contained 3448 different corneal images. Furthermore, it shows how an unbalanced dataset affects the performance of classifiers, where the data are balanced using the resampling approach. Finally, the results obtained from CNN networks trained on the balanced dataset are compared to those obtained from CNN networks trained on the imbalanced dataset. For performance, the system estimated the diagnosis accuracy, precision, and F1-score metrics. Lastly, some generated images were shown to an expert for evaluation and to see how well experts could identify the type of image and its condition. The expert recognized the image as useful for medical diagnosis and for determining the severity class according to the shape and values, by generating images based on real cases that could be used as new different stages of illness between healthy and unhealthy patients.

Prediction of copper ions adsorption by attapulgite adsorbent using tuned-artificial intelligence model.

Copper (Cu) ion in wastewater is considered as one of the crucial hazardous elements to be quantified. This research is established to predict copper ions adsorption (Ad) by Attapulgite clay from aqueous solutions using computer-aided models. Three artificial intelligent (AI) models are developed for this purpose including Grid optimization-based random forest (Grid-RF), artificial neural network (ANN) and support vector machine (SVM). Principal component analysis (PCA) is used to select model inputs from different variables including the initial concentration of Cu (IC), the dosage of Attapulgite clay (Dose), contact time (CT), pH, and addition of NaNO3 (SN). The ANN model is found to predict Ad with minimum root mean square error (RMSE = 0.9283) and maximum coefficient of determination (R2 = 0.9974) when all the variables (i.e., IC, Dose, CT, pH, SN) were considered as input. The prediction accuracy of Grid-RF model is found similar to ANN model when a few numbers of predictors are used. According to prediction accuracy, the models can be arranged as ANN-M5> Grid-RF-M5> Grid-RF-M4> ANN-M4> SVM-M4> SVM-M5. Overall, the applied statistical analysis of the results indicates that ANN and Grid-RF models can be employed as a computer-aided model for monitoring and simulating the adsorption from aqueous solutions by Attapulgite clay.

Interaction modeling and classification scheme for augmenting the response accuracy of human-robot interaction systems.

BACKGROUND: Human-robot interaction (HRI) is becoming a current research field for providing granular real-time applications and services through physical observation. Robotic systems are designed to handle the roles of humans and assist them through intrinsic sensing and commutative interactions. These systems handle inputs from multiple sources, process them, and deliver reliable responses to the users without delay. Input analysis and processing is the prime concern for the robotic systems to understand and resolve the queries of the users. OBJECTIVES: In this manuscript, the Interaction Modeling and Classification Scheme (IMCS) is introduced to improve the accuracy of HRI. This scheme consists of two phases, namely error classification and input mapping. In the error classification process, the input is analyzed for its events and conditional discrepancies to assign appropriate responses in the input mapping phase. The joint process is aided by a linear learning model to analyze the different conditions in the event and input detection. RESULTS: The performance of the proposed scheme shows that it is capable of improving the interaction accuracy by reducing the ratio of errors and interaction response by leveraging the information extraction from the discrete and successive human inputs. CONCLUSION: The fetched data are analyzed by classifying the errors at the initial stage to achieve reliable responses.

Robotic Mounted Rail Arm System for implementing effective workplace safety for migrant workers.

BACKGROUND: The selection of orders is the method of gathering the parts needed to assemble the final products from storage sites. Kitting is the name of a ready-to-use package or a parts kit, flexible robotic systems will significantly help the industry to improve the performance of this activity. In reality, despite some other limitations on the complexity of components and component characteristics, the technological advances in recent years in robotics and artificial intelligence allows the treatment of a wide range of items. OBJECTIVE: In this article, we study the robotic kitting system with a Robotic Mounted Rail Arm System (RMRAS), which travels narrowly to choose the elements. RESULTS: The objective is to evaluate the efficiency of a robotic kitting system in cycle times through modeling of the elementary kitting operations that the robot performs (pick and room, move, change tools, etc.). The experimental results show that the proposed method enhances the performance and efficiency ratio when compared to other existing methods. CONCLUSION: This study with the manufacturer can help him assess the robotic area performance in a given design (layout and picking a policy, etc.) as part of an ongoing project on automation of kitting operations.

Security robot for the prevention of workplace violence using the Non-linear Adaptive Heuristic Mathematical Model.

BACKGROUND: Nowadays, workplace violence is found to be a mental health hazard and considered a crucial topic. The collaboration between robots and humans is increasing with the growth of Industry 4.0. Therefore, the first problem that must be solved is human-machine security. Ensuring the safety of human beings is one of the main aspects of human-robotic interaction. This is not just about preventing collisions within a shared space among human beings and robots; it includes all possible means of harm for an individual, from physical contact to unpleasant or dangerous psychological effects. OBJECTIVE: In this paper, Non-linear Adaptive Heuristic Mathematical Model (NAHMM) has been proposed for the prevention of workplace violence using security Human-Robot Collaboration (HRC). Human-Robot Collaboration (HRC) is an area of research with a wide range of up-demands, future scenarios, and potential economic influence. HRC is an interdisciplinary field of research that encompasses cognitive sciences, classical robotics, and psychology. RESULTS: The robot can thus make the optimal decision between actions that expose its capabilities to the human being and take the best steps given the knowledge that is currently available to the human being. Further, the ideal policy can be measured carefully under certain observability assumptions. CONCLUSION: The system is shown on a collaborative robot and is compared to a state of the art security system. The device is experimentally demonstrated. The new system is being evaluated qualitatively and quantitatively.

RERS-CC: Robotic facial recognition system for improving the accuracy of human face identification using HRI.

BACKGROUND: Human-Computer Interaction (HCI) is incorporated with a variety of applications for input processing and response actions. Facial recognition systems in workplaces and security systems help to improve the detection and classification of humans based on the vision experienced by the input system. OBJECTIVES: In this manuscript, the Robotic Facial Recognition System using the Compound Classifier (RERS-CC) is introduced to improve the recognition rate of human faces. The process is differentiated into classification, detection, and recognition phases that employ principal component analysis based learning. In this learning process, the errors in image processing based on the extracted different features are used for error classification and accuracy improvements. RESULTS: The performance of the proposed RERS-CC is validated experimentally using the input image dataset in MATLAB tool. The performance results show that the proposed method improves detection and recognition accuracy with fewer errors and processing time. CONCLUSION: The input image is processed with the knowledge of the features and errors that are observed with different orientations and time instances. With the help of matching dataset and the similarity index verification, the proposed method identifies precise human face with augmented true positives and recognition rate.

ADA-SR: Activity detection and analysis using security robots for reliable workplace safety.

BACKGROUND: Human-Robot Interaction (HRI) has become a prominent solution to improve the robustness of real-time service provisioning through assisted functions for day-to-day activities. The application of the robotic system in security services helps to improve the precision of event detection and environmental monitoring with ease. OBJECTIVES: This paper discusses activity detection and analysis (ADA) using security robots in workplaces. The application scenario of this method relies on processing image and sensor data for event and activity detection. The events that are detected are classified for its abnormality based on the analysis performed using the sensor and image data operated using a convolution neural network. This method aims to improve the accuracy of detection by mitigating the deviations that are classified in different levels of the convolution process. RESULTS: The differences are identified based on independent data correlation and information processing. The performance of the proposed method is verified for the three human activities, such as standing, walking, and running, as detected using the images and sensor dataset. CONCLUSION: The results are compared with the existing method for metrics accuracy, classification time, and recall.

Newly explored machine learning model for river flow time series forecasting at Mary River, Australia.

Hourly river flow pattern monitoring and simulation is the indispensable precautionary task for river engineering sustainability, water resource management, flood risk mitigation, and impact reduction. Reliable river flow forecasting is highly emphasized to support major decision-makers. This research paper adopts a new implementation approach for the application of a river flow prediction model for hourly prediction of the flow of Mary River in Australia; a novel data-intelligent model called emotional neural network (ENN) was used for this purpose. A historical dataset measured over a 4-year period (2011-2014) at hourly timescale was used in building the ENN-based predictive model. The results of the ENN model were validated against the existing approaches such as the minimax probability machine regression (MPMR), relevance vector machine (RVM), and multivariate adaptive regression splines (MARS) models. The developed models are evaluated against each other for validation purposes. Various numerical and graphical performance evaluators are conducted to assess the predictability of the proposed ENN and the competitive benchmark models. The ENN model, used as an objective simulation tool, revealed an outstanding performance when applied for hourly river flow prediction in comparison with the other benchmark models. However, the order of the model, performance wise, is ENN > MARS > RVM > MPMR. In general, the present results of the proposed ENN model reveal a promising modeling strategy for the hourly simulation of river flow, and such a model can be explored further for its ability to contribute to the state-of-the-art of river engineering and water resources monitoring and future prediction at near real-time forecast horizons.

Drought index prediction using advanced fuzzy logic model: Regional case study over Kumaon in India.

A new version of the fuzzy logic model, called the co-active neuro fuzzy inference system (CANFIS), is introduced for predicting standardized precipitation index (SPI). Multiple scales of drought information at six meteorological stations located in Uttarakhand State, India, are used. Different lead times of SPI were computed for prediction, including 1, 3, 6, 9, 12, and 24 months, with inputs abstracted by autocorrelation function (ACF) and partial-ACF (PACF) analysis at 5% significance level. The proposed CANFIS model was validated against two models: classical artificial intelligence model (e.g., multilayer perceptron neural network (MLPNN)) and regression model (e.g., multiple linear regression (MLR)). Several performance evaluation metrices (root mean square error, Nash-Sutcliffe efficiency, coefficient of correlation, and Willmott index), and graphical visualizations (scatter plot and Taylor diagram) were computed for the evaluation of model performance. Results indicated that the CANFIS model predicted the SPI better than the other models and prediction results were different for different meteorological stations. The proposed model can build a reliable expert intelligent system for predicting meteorological drought at multi-time scales and decision making for remedial schemes to cope with meteorological drought at the study stations and can help to maintain sustainable water resources management.

Input attributes optimization using the feasibility of genetic nature inspired algorithm: Application of river flow forecasting.

In nature, streamflow pattern is characterized with high non-linearity and non-stationarity. Developing an accurate forecasting model for a streamflow is highly essential for several applications in the field of water resources engineering. One of the main contributors for the modeling reliability is the optimization of the input variables to achieve an accurate forecasting model. The main step of modeling is the selection of the proper input combinations. Hence, developing an algorithm that can determine the optimal input combinations is crucial. This study introduces the Genetic algorithm (GA) for better input combination selection. Radial basis function neural network (RBFNN) is used for monthly streamflow time series forecasting due to its simplicity and effectiveness of integration with the selection algorithm. In this paper, the RBFNN was integrated with the Genetic algorithm (GA) for streamflow forecasting. The RBFNN-GA was applied to forecast streamflow at the High Aswan Dam on the Nile River. The results showed that the proposed model provided high accuracy. The GA algorithm can successfully determine effective input parameters in streamflow time series forecasting.

Efficiency evaluation of reverse osmosis desalination plant using hybridized multilayer perceptron with particle swarm optimization.

The scarcity of freshwater causes the necessity for water delineation of brackish water. Reverse osmosis (RO) is one of the popular strategies characterized with lower cost and simple processing procedure compared to the other desalination techniques. The current research is conducted to investigate the efficiency the RO process based on one-week advance prediction of total dissolved solids (TDS) and permeate flow rate for Sistan and Bluchistan provinces located in Iran region. The water parameters including pH, feed pressure temperature, and conductivity are used to construct the prediction matrix. A newly hybrid data-intelligence (DI) model called multilayer perceptron hybridized with particle swarm optimization (MLP-PSO) is developed for the investigation. The potential of the proposed MLP-PSO model is validated against two predominate DI models including support vector machine (SVM) and M5Tree (M5T) models. The results evidenced the potential of the proposed MLP-PSO model over the SVM and M5T models in predicting the TDS and permeate flow rate. In addition, the proposed model attained lower uncertainty for the simulated data. Overall, the feasibility of the hybridized MLP-PSO achieved remarkable predictability for the RO process.

Laundry wastewater treatment using a combination of sand filter, bio-char and teff straw media.

Numerous researchers have expressed concern over the emerging water scarcity issues around the globe. Economic water scarcity is severe in the developing countries; thus, the use of inexpensive wastewater treatment strategies can help minimize this issue. An abundant amount of laundry wastewater (LWW) is generated daily and various wastewater treatment researches have been performed to achieve suitable techniques. This study addressed this issue by considering the economic perspective of the treatment technique through the selection of easily available materials. The proposed technique is a combination of locally available absorbent materials such as sand, biochar, and teff straw in a media. Biochar was prepared from eucalyptus wood, teff straw was derived from teff stem, and sand was obtained from indigenous crushed stones. In this study, the range of laundry wastewater flow rate was calculated as 6.23-17.58 m3/day; also studied were the efficiency of the media in terms of the removal percentage of contamination and the flux rate. The performances of biochar and teff straw were assessed based on the operation parameters and the percentage removal efficiency at different flux rates; the assessment showed 0.4 L/min flux rate to exhibit the maximum removal efficiency. Chemical oxygen demand, biological oxygen demand, and total alkalinity removal rate varied from 79% to ≥83%; total solids and total suspended solids showed 92% to ≥99% removal efficiency, while dissolved oxygen, total dissolved solids, pH, and electrical conductivity showed 22% to ≥62% removal efficiency. The optimum range of pH was evaluated between 5.8-7.1. The statistical analysis for finding the correlated matrix of laundry wastewater parameters showed the following correlations: COD (r = -0.84), TS (r = -0.83), and BOD (r = -0.81), while DO exhibited highest negative correlation. This study demonstrated the prospective of LWW treatment using inexpensive materials. The proposed treatment process involved low-cost materials and exhibited efficiency in the removal of contaminants; its operation is simple and can be reproduced in different scenarios.