Soft Computing Letters最新文献

The availability of non-renewable fossil fuels in Jordan continues to decrease, which increases reliance on energy sources, such as, methane gas produced from municipal solid waste (MSW). Furthermore, during the COVID-19 pandemic, solid wastes were significantly increased, especially in lockdown periods and this increase requires an immediate response to this global emergency by improving MSW management system. Unfortunately, little previous research efforts have been directed to propose optimization models that optimize concurrently economic and environmental aspects with the utilization of the available resources from transportation trucks of different types and capacities. This research, therefore, develops an optimization model for efficient MSW management system to increase the percentage of waste transported from multiple depots to anaerobic digestion plants (ADP) or recycling centers. The objective function of the optimization model is two-fold; maximizing quantities of transported waste and minimizing both transportation costs and greenhouse gas (GHG) emissions generated from different types of transport trucks over a six-day period. A case study was presented, where the optimization results showed that on average 1236.36 mega Watt-hour (MWh) of energy potential at a minimal average processing cost of 165.22 $/ton could be generated from transported 3540 tons of waste over six days. Such energy can be utilized to promote sustainability and develop an eco-city powered by renewable energy. In conclusion, the proposed model is found efficient in enhancing the performance of the existing MSW and results in significant reductions in environmental impacts and transportation costs and maximizing trucks and facilities utilizations.

The probabilistic linguistic term set (PLTS) has been widely used in multiple criteria group decision making (MCGDM) problems where the linguistic information is uncertain and hesitant. To reflect the different preferences and uncertainties, we propose a new PLTS with probability in the form of Pythagorean fuzzy set (PFS), called Pythagorean fuzzy probabilistic linguistic term set (PFPLTS). In addition, considering the information integrity, uncertainty and DMs' preferences, the operation and aggregation operators for PFPLTS are introduced. Then, the weight method based on minimum deviation and dual ideal point-vector projection is proposed, which considers the time-varying characteristics of the weights and combines multi-dimensional influencing factors. Next, the psychological distance measure is proposed by dividing the psychological space into multiple vectors. Based on the proposed dynamic weight method, three psychological distance measures and TOPSIS method, we develop a dynamic Pythagorean fuzzy probabilistic linguistic TOPSIS method with psychological distance (Psy-TOPSIS), the psychological index ranges from 1 to 40. Finally, a practical case, site selecting of COVID-19 vaccination center, is given and compared with three approaches to illustrate the effectiveness and practicality of PFPLTS and the proposed decision-making method.

Forest fire is considered as one of the main cause of the environmental hazard that provides many negative effects. Effective Forest Fire prediction models help to take the necessary steps to prevent forest fire and its negative effects. Existing methods of Cascade Correlation Network (CCN), Radial Basis Function (RBF) and Support Vector Machine (SVM) were applied for the forest fire prediction. Existing methods have the limitations of over fitting problems and lower efficiency in prediction. Existing methods in forest fire prediction have lower efficiency in large dataset due to overfitting problem in the models. The parallel SVM method is developed in this research for reliable performance of the Forest Fire Prediction. Conventional SVM has a higher efficiency in predicting the small fire and has lower efficiency in predicting large fire. The SPARK and PySpark were applied to perform the data segmentation and feature selection in the prediction process. A parallel SVM model is developed to train the meteorological data and predict the forest fire effectively. The parallel SVM model reduces the computational time and high storage required for the analysis. Parallel SVM considers the Forecast Weather Index (FWI) and some weather parameters for the prediction of a forest fire. The parallel SVM model is evaluated on the Indian and Portugal data to analyze the efficiency of the model. The parallel SVM model has the 63.45 RMSE and SVM method has 63.5 RMSE in the Portugal data.

Data mining is an emerging technology where researchers explore innovative ideas in different domains, particularly detecting anomalies. Instances in the dataset which considerably deviate from others by their common patterns are known as anomalies. The state of being ambiguous and not affording certainty of data exists in this world of nature. Rough Set Theory is a proven methodology which deals with ambiguity and uncertainty of data. Research works that have been done until this point were focused on numeric or categorical type, which fails when the attributes are mixed type. By using fuzzy proximity and ordering relations, the numerical data has been converted to categorical data. This article presented an idea for detecting outliers in mixed data where the weighted density values of attributes and objects are calculated. The proposed approach has been compared with existing outlier detection methods by taking the hiring dataset as an example and benchmarked with Harvard dataverse datasets to prove its efficiency and performance.

Data interchange through internet becomes an eminent technique and hence data security has become a big challenge in the field of communication with the increased use of internet. Demand for data authentication and effective means to control data integrity has been steadily increasing. Such a demand is due to the ease with which digital data can be tampered. Thus, cryptography and watermarking can be replaced with steganography for secure data communication and data privacy. In this paper, the carrier image is selected such that the payload/secret image and least significant bits of carrier image are matched with larger degree of compatibility and the hiding process introduces negligible changes in the resulted stego image based on genetic algorithm. In the proposed method we have achieved 30 to 40% improvements in the performance when compared to different existing methods. Selection of a suitable cover image and hiding the secret data to enhance the imperceptibility is a very challenging task. Genetic algorithm is used to ease the work of exploring an impossible task of selection from the trillions and millions of combinations.

A decision maker may hold multiple viewpoints regarding the relative priorities of criteria simultaneously, but this has rarely been considered in past studies. Therefore, this study proposes a bi-objective analytic hierarchy process (AHP)–mixed integer nonlinear programming (MINLP)–genetic algorithm (GA) approach. First, AHP is applied to decompose the decision maker's judgment matrix into several sub-judgment matrices. Each sub-judgment matrix represents a single viewpoint and generates a priority set. To generate diversified priority sets, a bi-objective MINLP problem is solved using a GA, and multiple alternatives can be selected based on these priority sets. The proposed approach has been applied to the real case of choosing diversified alternative suppliers amid the COVID-19 pandemic to assess its effectiveness. Several existing methods were also applied to this case for comparison. Experimental results showed that only the proposed approach was able to diversify the recommended alternative suppliers that were simultaneously optimal, thereby enhancing decision-making flexibility. In addition, the application of GA increased the solution efficiency by up to 75%.

With the innovative development of advanced technology in the field of medical, there is an enlargement in the generation of other problems such as management of biomedical waste. Hazardous waste generated from hospitals is required to be managed within time and it can be done effectively using some computer science technology. In the proposed methodology, Biomedical Waste (BMW) problem is solved with the consideration of route optimization. Route optimization is important in BMW management because while transporting the BMW from hospital to depot (disposal site) there are many types of risks associated with that route like traffic, vehicle failure, road accident etc. To avoid the dangerous effects of BMW on humans and environment, it is necessary to optimize the distance. It can help in promoting healthy and risk free life. This paper addresses the problem of finding the shortest path using Cohort Intelligence algorithm for BMW management with the consideration of human risk.

Terrorism can be described as the use of violence against persons or properties to intimidate or coerce a government or its citizens to some certain political or social objectives. It is a global problem which has led to loss of lives and properties and known to have negative impacts on tourism and global economy. Terrorism has also been associated with high level of insecurity and most nations of the world are interested in any research efforts that can reduce its menace. Most of the research efforts on terrorism have focused on measures to fight terrorism or how to reduce the activities of terrorists but there are limited efforts on terrorism prediction. The aim of this work is to develop an ensemble machine learning model which combines Support Vector Machine and K-Nearest Neighbor for prediction of continents susceptible to terrorism. Data was obtained from Global Terrorism Database and data preprocessing included data cleaning and dimensionality reduction. Two feature selection techniques, Chi-squared, Information Gain and a hybrid of both were applied to the dataset before modeling. Ensemble machine learning models were then constructed and applied on the selected features. Chi-squared, Information Gain and the hybrid-based features produced an accuracy of 94.17%, 97.34% and 97.81% respectively at predicting danger zones with respective sensitivity scores of 82.3%, 88.7% and 92.2% and specificity scores of 98%, 90.5% and 99.67% respectively. These imply that the hybrid-based selected features produced the best results among the feature selection techniques at predicting terrorism locations. Our results show that ensemble machine learning model can accurately predict terrorism locations.

A patient-specific novel systematic methodology is described in this study for automatic seizure detection from raw electroencephalogram (EEG) signals. Filtering process by means of band-pass finite impulse response (FIR) filter with the frequency range of 0.5–40 Hz is implemented at the outset to eliminate different artifacts and noises mixed with raw EEG signals. As EEGs are highly non-linear and non-stationary signals in nature, discrete wavelet transform (DWT) is then used to analyze the signals in time-frequency domain. DWT with four level decomposition is performed using db6 mother wavelet for feature extraction. A new feature set, composed of eleven non-linear statistical features extracted from each sub-bands resulting from due to wavelet decomposition, is then fed to the input of artificial neural network (ANN) to classify the signal accurately. Finally, a novel algorithm named sequential window algorithm is carried out to improve the classification performance. 99.44% mean classification accuracy, 80.66% average sensitivity, 4.12 s mean latency and 0.2% average false positive rate (FPR) are achieved in this study. This study successfully reduces the latency time with more accuracy and significantly low FPR.

Product design activities are predicated on fuzzy modelling, given that verbalising and interpreting engineering requirements are inherently fuzzy processes. The aim of this paper is to present a method for fuzzy intelligent requirement engineering from natural language to Computer-Aided Design (CAD) models. The field exploring the dynamics of computational processes from fuzzy linguistic modelling to fuzzy design modelling is complex and remains under-explored. No existing research has been identified which focuses specifically on fuzzy requirements engineering from natural language to CAD modelling. This paper seeks to address this by providing a design formalisation system based on five key principles. These principles are used to set out a computing procedure which follows a method broken up into six phases. The results of these six phases are fuzzy semantic graphs, which provide engineering requirements according to reliable design information. The approach is put into practice using the fuzzy agent-based tool developed by the authors, called F-EGEON (Fuzzy Engineering desiGn sEmantics elabOration and applicatioN). The proposed method is illustrated through an application from the automotive industry.