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Fuzzy clustering especially fuzzy C -means (FCM) is considered as a useful tool in the processes of pattern recognition and knowledge discovery from a database; thus being applied to various crucial, socioeconomic applications. Nevertheless, the clustering quality of FCM is not high since this algorithm is deployed on the basis of the traditional fuzzy sets, which have some limitations in the membership representation, the determination of hesitancy and the vagueness of prototype parameters. Various improvement versions of FCM on some extensions of the traditional fuzzy sets have been proposed to tackle with those limitations. In this paper, we consider another improvement of FCM on the picture fuzzy sets, which is a generalization of the traditional fuzzy sets and the intuitionistic fuzzy sets, and present a novel picture fuzzy clustering algorithm, the so-called FC-PFS. A numerical example on the IRIS dataset is conducted to illustrate the activities of the proposed algorithm. The experimental results on various benchmark datasets of UCI Machine Learning Repository under different scenarios of parameters of the algorithm reveal that FC-PFS has better clustering quality than some relevant clustering algorithms such as FCM, IFCM, KFCM and KIFCM.

Designing optimization models and meta-heuristic algorithms for minimization of traveling routes of vehicles in solid waste collection has been gaining interest in environmental modeling. The computer models and methods are useful to bring out specific strategies for prevention and precaution of possible disasters that could be foreseen worldwide. This paper proposes a new Spatial Geographic Information System (GIS)-based Genetic Algorithm for optimizing the route of solid waste collection. The proposed algorithm, called SGA, uses a modified version of the original Dijkstra algorithm in GIS to generate optimal solutions for vehicles. Then, a pool of solutions, which are optimal routes of all vehicles, is encoded in Genetic Algorithm. It is iteratively evolved to a better one and finally to the optimal solution. Experiments on the case study at Sfax city in Tunisia are performed to validate the performance of the proposal. It has been shown that the proposed method has better performance than the practical route and the original Dijkstra method.

In a developed society, people have more concerned about their health. Thus, improvement of medical field application has been one of the greatest active study areas. Medical statistics show that heart disease is the main reason for morbidity and death in the world. The physician’s job is difficult because of having too many factors to analyze in the diagnosis of heart disease. Besides, data and information gained by the physician for diagnosis are often partial and immersed. Recently, health care applications with the Internet of Things (IoT) have offered different dimensions and other online services. These applications have provided a new platform for millions of people to receive benefits from the regular health tips to live a healthy life. In this paper, we propose a novel framework based on computer supported diagnosis and IoT to detect and monitor heart failure infected patients, where the data are attained from various other sources. The proposed healthcare system aims at obtaining better precision of diagnosis with ambiguous information. We suggest neutrosophic multi criteria decision making (NMCDM) technique to aid patient and physician to know if patient is suffering from heart failure. Furthermore, through dealing with the uncertainty of imprecision and vagueness resulted from the symmetrical priority scales of different symptoms of disease, users know what extent the disease is dangerous in their body. The proposed model is validated by numerical examples on real case studies. The experimental results indicate that the proposed system provides a viable solution that can work at wide range, a new platform to millions of people getting benefit over the decreasing of mortality and cost of clinical treatment related to heart failure.

Mobile Ad hoc Networks (MANETs) have gained great interests owing to their dynamic and smoothness of exploitation. Conversely, the wireless and energetic nature adds exposed to different types of protection attacks than the other kind of networks. In this kind of attacks, it is essential to expand proficient intrusion-detection system to prevent MANET from different attacks. In this paper, we recommend a new intrusion-detection system called Modified Zone Based Intrusion Detection System (MZBIDS) for MANETs. Evaluated to contemporary methodologies, MZBIDS exhibits superior malicious behavior-detection ratios in convinced situations whereas it may not significantly influence performance of entire network.

Neutrosophic set is a powerful general formal framework which generalizes the concepts of classic set, fuzzy set, interval-valued fuzzy set, intuitionistic fuzzy set, etc. Recent studies have developed systems with complex fuzzy sets, for better designing and modeling real-life applications. The single-valued complex neutrosophic set, which is an extended form of the single-valued complex fuzzy set and of the single-valued complex intuitionistic fuzzy set, presents difficulties to defining a crisp neutrosophic membership degree as in the single-valued neutrosophic set. Therefore, in this paper we propose a new notion, called interval complex neutrosophic set (ICNS), and examine its characteristics. Firstly, we define several set theoretic operations of ICNS, such as union, intersection and complement, and afterward the operational rules. Next, a decision-making procedure in ICNS and its applications to a green supplier selection are investigated. Numerical examples based on real dataset of Thuan Yen JSC, which is a small-size trading service and transportation company, illustrate the efficiency and the applicability of our approach.

India is one of the most polluted countries in the world, where several major cities are facing serious environmental consequences as a result of rapid pollution growth. The objective of this research is to analyze air pollution trends with respect to various geographical locations, in order to have a global view of the damage caused, so that appropriate actions can be developed in the future to prevent air pollution. In this regard, the polluted database was established based on the data provided by the Central Pollution Control Board; Ministry of Environment, Forest, and Climate Change (India). These data demonstrate the annual growth of SO2, NOx, and particulate matter (PM) 2.5 from 2015 to 2018 and were recorded at various monitoring stations in three cities, namely, Delhi, Bengaluru, and Chennai. The results show that SO2, NOx, and PM 2.5 were from different transport modes, both small or large-scale power generations (from diesel, coal and gas plant), industries, constructions, and domestic cooking. Overall, there was an increasing trend, day by day, in India. The result categorized the considered areas into the following four classes: critically polluted (CP), highly polluted (HP), moderately polluted (MP), and low polluted (LP). The results will assist in the assessment of pollution for the cities investigated in this research.

In real world applications, soft computing is an inspirational domain for encoding imprecision and uncertainty. Soft computing procedures integrated with medical applications can support the existing medical systems to allow solutions for unsolvable problems. Fuzzy techniques have extensive solutions for the medical domain applications; however incorporating a new neutrosophic approaches in the medical domain proves its superiority. The current study reported the main neutrosophic sets (NS) definitions along with different medical applications based on NS. In addition, an extensive discussion for the possibility of prolonging the abilities of the fuzzy systems using the neutrosophic systems was included. The preceding studies established that the NS has a significant role in medical images de-noising, clustering, and segmentation. As a future scope, it was suggested that the neutrosophic medical systems can be exploited for neutrosophic scores; continuous truth/indeterminate/falsity versions of conventional score schemes. The integrated methods of the NS in medical domain would lead to tabular or rule-based mapping from input to output variables. The qualitative simulation of the reported studies established that the neutrosophic model based diagnosis is promising aspirants for future research. Furthermore, the current work highlighted the main medical image processes that can be developed using the NS, including de-noising, thresholding, segmentation, clustering and classification. The general algorithms that can be used to include NS in each task were proposed.

This paper proposes a new technique based on Direct Sequence Code Division Multiple Access for underwater acoustically wireless transmission with excessive transmission rate. Environment of subsea is challenging for wireless communication because the medium in which waves are propagating is not air. In fact, it is propagated through fractions of water having different densities. Finding out various techniques for multipath access targeting the physical layer of Acoustic Sensor Networks is indeed necessary. The recent approaches have suggested that coded modulation techniques with exploited diversity are highly preferred in order to enhance the dependability of the acoustic link in different multipath channels. The proposed technique divides the channel into sub ones and transmits information via those sub channels. In variety-spectrum, a signal in a bandwidth is unfold within frequency domain and broad bandwidth. Experimental results show that Bit Error Rate (BER) of this method is better than that of channel equalization in the respective systems.

Mobile ad-hoc network (MANET) plays a significant role in the field of communication. Due to the dynamic movement of nodes, the network infrastructure is frequently changed. All nodes have the capacity to configure themselves and are communicated either directly through some intermediate nodes based on signal strength or through multi-hop routing. However, selection of the intermediate nodes will increase the routing overload in the route discovery procedure. Destination nodes are selected using intermediate nodes for broadcasting data packets with link scalability. The previous works for this problem have limitations such as they are not flexible to deliver the Quality of Service in the network model, and the possibility of packet delivery is less. In this paper, we propose Fault-Tolerant Disjoint Multipath Distance Vector Routing Algorithm (FD-AOMDV) that sprints path discovery phase with a reduced amount of delay. It finds disjoint paths in a way that routing overloads decrease considerably. FD-AOMDV can increase the scalability by reducing the routing overload when the latest route is established. Moreover, owing to the mobility of the node in MANETs, subsequent breakages of a link will cause the active path disconnection and also enlarge the routing overload. The simulation results prove that the proposed work reduces the routing overload, decreases the end-to-end delay, and reduces the packet delivery ratio compared with AOMDV and ZD-AOMDV on Network Simulator 2.

Air pollutants can cause multifaceted harm to the human body. Respiratory diseases and immunology dysfunction are some of its main manifestations. Forecasting the air quality of a country is important to allow the government to take preventive measure. In this research, the artificial neural network (ANN), autoregressive integrated moving average (ARIMA), trigonometric regressors, Box-Cox transformation, ARMA errors, trend and seasonality (TBATS) and several fuzzy time series (FTS) models are utilized in the forecasting of air pollution index (API) of Kuala Lumpur, Malaysia, for the year 2017. Six years of daily API data for Kuala Lumpur from the year 2012 to the year 2017 for the Cheras observation station in Kuala Lumpur has been selected as the dataset of this research. The mean absolute percentage error (MAPE), root mean square error (RMSE) and computational time have been used as the performance evaluation metrics for the models and these values were calculated for each of the chosen forecasting models. A brief but comprehensive comparative study of the results obtained from each of the chosen model is presented in order to identify the most effective model to forecast API values. It was found that the fuzzy time series models outperformed the other models in terms of accuracy of forecasted values and computation time. Specifically, the Singh fuzzy time series model was found to be the most accurate and efficient forecasting model with RMSE of 1.4704 and MAPE of 4.364%.