Evolvingfuzzy systems (EFSs), as another key branch of computational intelligence, are viewed as a powerful tool for handling complex problems with both measurement and motion uncertainties . EFSs have demonstrated success in a wide variety of real-world applications – and is now an intensively studied area –. Compared with DNNs  and alternative mainstream approaches, e.g., support vector machine , random forest , EFSs perform human-like reasoning and decision-making , and their inner system structure is simpler and more transparent. In addition, the majority of EFSs are designed for processing streaming data in a “one pass” manner so that they can efficiently transform data into knowledge presented in a human-interpretable form. Currently, there have been a number of successful EFSs introduced, which include, but are not limited to, DENFIS , eTS , SAFIS , eClass , FLEXFIS , SOFMLS , PANFIS , GENEFIS , McIT2FIS , eT2Class  and CNFS . Interested readers may refer to the recent survey  for more details regarding EFSs.
A decade of exponential growth in wireless networks has profoundly impacted our lifestyle, from cellular telephones to wireless Internet access. A wireless network without any fixed infrastructure is referred to as a mobile ad hoc network (MANET), as the wireless nodes are capable of moving freely. It is a mobile, wireless, multi-hop network that operates without the benefit of any existing infrastructure, except for the nodes themselves . Such networks are assumed to be self-forming and self-healing. These unique characteristics allow them to be used in special applications such as the army, emergency/ rescue operations, habitat monitoring etc. Routing in such networks is challenging because typical routing protocols do not operate efficiently in the presence of frequent movements, intermittent connectivity, and network splits/joints. Moreover, the use of wireless links makes these networks very vulnerable to security attacks, ranging from passive eavesdropping to active interfering. Attacks against the network may come from malicious nodes that are not part of the network and are trying to join the network without authorization. Such nodes are typically called outsiders. Networks are protected from malicious outsiders through the use of cryptographic techniques. Such techniques allow nodes to securely verify the identity of other nodes, and can therefore; try to prevent any harm being caused by the malicious outsiders. Attacks also come from nodes that are authorized to be part of the network and are typically called insiders. Insider nodes may launch attacks because they have been compromised by an unauthorized user (e.g. hacker) through some form of remote penetration, or have been physically captured by a malicious user.
The FR model, as a bivariate statistical model, can be used as a simple spatial tool to calculate the probabilistic relationship between independent and dependent variables, which includes several categorized maps (Oh et al., 2011). This method was used to prepare a groundwater potential mapping map by Ozdemir (2011). The FR value of occurrence probability for a phenomenon is in the presence of a specific property. The FR approach is based on the observed relationship between the distribution of flood and flood conditioning factors. The FR of each layer is calculated from each criterion according to Eq. (3).
In the proposed MICE approach, as described above we expanded the original dataset into 77 different training sets using scaling and rotation. With the HOG and GIST features extracted from each training set, we trained 154 ALMMo-0 systems in parallel for each digit. The accuracy our MICE classifier that contains a set of fully transparent and interpretable fuzzy IF…THEN…rules of AnYa type  is 99.32% (only 68 errors for the 10000 validation images). We also compared our approach with the best reported state-of-art techniques in terms of accuracy, time and complexity, reproducibility, parallelization, evolving capability and presented it in Table I.
From Tables 2 and 3, we can see that feature selection boosts performance of the system by ∼ 4 − 6%. When using all 49 features in UNSW and 41 features in NSL- KDD datasets, the accuracy is reduced. This means that there are some redundant features present in both the datasets, which the ExtraTrees classifier is able to remove. Table 4 tabulates the feature scores generated by the ExtraTrees classifier for the DoS type of attack in the UNSW dataset, in ascending order. The most important characteristic of using an ensemble-based approach to feature selection is that the most important features for each attack are considered separately. This is an indis- pensable property because for detecting a particular type of attack, some features might be considered irrelevant and, as a result, will be discarded. But, for another type of attack, some of those discarded features might be con- sidered important. So, it is highly desirable to have an ensemble approach to feature selection as well, so that fea- tures can be selected for the detection of different kinds of attacks separately.
Fuzzy logic is determined as a set of mathematical principles for knowledge representation based on degrees of membership rather than on crisp membership of classical binary logic . Fuzzy systems are a part of soft computing that works on the discipline of vagueness and gives results in an interpretable manner. Fuzzysystem makes use of fuzzy set theory, fuzzy reasoning and inference mechanism so that such systems can be employed in various applications. In classical set theory an object can either a member of a given set or not while fuzzy set theory allows an object to belong to a set with a certain degree. Fuzzysystem models fuzzy boundaries of linguistic terms by introducing gradual membership . Tsukamoto fuzzyinferencesystem are solving the problem in If-Then Rules Form. In Tsukamoto Method, each consequence of If-Then Rules must be represented by a fuzzy set with monotonous membership function. Consequently, the interference outputs of each rule are crisply presented in line with α-predicate . In our earlier works we find some limitation of Tsukamoto FuzzyInferenceSystem in producing the Crisp Value.
1243 | P a g e inter-collision distance (m/s) & output is the brake shoe pressure (lbs per unit time). Each input function is defined with seven membership functions (five triangular & two trapezoidal) and Centroid defuzzification method is used for extracting the crisp output. The fuzzy controller takes the decision with reference to the speed and collision distance between the vehicles. The hardware implementation of fuzzy controller for specific application  is presented in simple way in order to realize the proposed model. In order to detect collision distance of the vehicle, suitable non-contact type sensors and wheel speed sensors can be utilized. Once the detection is done, these systems either can provide an alert to the driver or take action automatically without any driver input. Collision avoidance through braking is possible at low vehicle speeds (e.g. below 50 km/h), but it becomes very difficult at higher vehicle speeds.
In the proposed system we have made a Mamdani FIS, with 13 rule bases. 17 chemical analytes available in the blood were computed in the FIS. Each analytes value were classified as low, Normal and High using triangular membership functions. Disease manifestations are done by the system, as per the rule bases available. All the diseases, that were diagnosed by the proposed system has two or more analytes in the abnormal range. This makes the system more accurate than reaching a final diagnosis from the values of one or two alone. IV RESULT AND DISCUSSION
Shafigh, A. S. Abdollahi , K. Kassler Andeas J  proposed Fuzzy logic control method to improve the performance and reliability of the multicast routing protocols in MANET. Strong and small forwarding group is established to decrease the resource consumption and higher stability of the delivery structure. A forwarding group is made out of set of strong /weak nodes. Fuzzy logic is proposed to distinguish the strong and weak nodes in the network. Join query packet is periodically broadcasted to update the routes in the network. An intermediate node receives a non-duplicate join query; it stores the upstream node ID into the routing table and rebroadcasts the packet. A node receives a join query message; it needs to fuzzyfys the parameters such as bandwidth, node speed and power level of previous node. The value of previous node's parameter is used to classify them as low, medium or high. After fuzzification, inference process is used to derive the probability of caching and forwarding the join query to other nodes. Using fuzzy based approach only links and nodes which are more robust or have more available power will participate in the forwarding mesh.
Asma Ben Abacha, Chaitanya Shivade, and Dina Demner-Fushman. 2019. Overview of the mediqa 2019 shared task on textual inference, question entail- ment and question answering. ACL-BioNLP 2019. Waleed Ammar, Dirk Groeneveld, Chandra Bhagavat- ula, Iz Beltagy, Miles Crawford, Doug Downey, Ja- son Dunkelberger, Ahmed Elgohary, Sergey Feldman, Vu Ha, Rodney Kinney, Sebastian Kohlmeier, Kyle Lo, Tyler Murray, Hsu-Han Ooi, Matthew E. Peters, Joanna Power, Sam Skjonsberg, Lucy Lu Wang, Chris Wil- helm, Zheng Yuan, Madeleine van Zuylen, and Oren Etzioni. 2018. Construction of the literature graph in semantic scholar. CoRR, abs/1805.02262.
There is an acute problem of online methods‟ development for data processing under conditions of significant uncertainty about data streams‟ properties. Another important point is the data are usually nonstationary, nonlinear, random and fuzzy; furthermore, there‟s no information about clusters‟ number and type which are formed with the data. Hybrid systems of computational intelligence may be an effective solution for this kind of problems, but most of the well-known systems which are widely used in these tasks are focused mostly on batch processing with a certain predefined number of classes. It seems appropriate to develop adaptive systems of computational intelligence that can adjust both their parameters and their structure.
Recently, multi-label classification (MLC) algorithms have been increasingly required by a diversity of applications, such as text categorization, web, and social media mining . It naturally emerged from the multiple meanings of many real- world objects and can be treated as the generalization of multi- class classification (also known as single-label classification), where an instance may have a set of relevant labels instead of only one label. In this paper, we adapt one of our recently published online multiclass classifier (named Online Variational Inference for multivariate Gaussians (VIGO))  to multi-label classification due to its demonstrated superior performance over several well-known methods in the literature. Although many batch MLC algorithms have been proposed in the literature, there is relatively little work being done on online (incremental) MLC . In particular, the demand for effective incremental methods is growing quickly in our big data era, where it is becoming increasingly impractical to store the entire training set in the main memory for batch learning. Moreover, offline methods cannot update its model on-the-fly and must be rebuilt whenever new data arrive, leading to costly operation in real-time applications with streaming data. On the other hand, online methods offer the essential ability of predictive models which can be trained on-the-fly after the arrival of every new data point and be ready to give predictions at any time if requested, by making use of a single/set of observations and then discarding them permanently before the next observations are used.
The advantages of ANFIS can be first described through the advantages of Fuzzy Logic . Fuzzy logic is conceptually easy to understand because the mathematical concepts described in Section 2.4 are intuitive. Fuzzy logic is flexible because each layer can add more functionality without starting the algorithm from scratch. Fuzzy logic is tolerant of imprecise data as it can model nonlinear functions of arbitrary complexity through the ANFIS. The most important advantage is that fuzzy logic is based on natural language. It is the basis for human communication because it is built on structures of qualitative description. In terms of the ANFIS itself, the advantage of the algorithm as discussed in Section 1.6 is its fast convergence speed. Smoothness is also guaranteed by interpolation. Also fuzzy sets as discussed in  are a depiction of prior knowledge into a set of constraints to reduce the optimization research space.
An attempt is made to construct a simple hybrid forecasting model consisting of ANN and ANFIS predictors by employing a weight factor to the outcomes of both ANN and ANFIS. Although ANN outperforms ANFIS in short-term wind speed forecasting, the hybrid method proves to be promising as it presents lower RMSE comparing to both ANFIS and ANN methods. Although this early attempt for constructing an ensemble method is successful, other approaches should be considered to find out whether or not more robust ensembles of ANN and ANFIS could result in better forecasting performances for short-term time-series prediction. The other area to be explored is the possibility of improving the performance of ANFIS itself, and whether using different structures of ANFIS could improve its performance.
The success of this work is accredited to many. Firstly, thanking the god and my parents for every- thing, my next acknowledgement goes to my supervisor-cum-guide, Dr. Jayaram Balasubramaniam, because of whom, I got to learn, understand thoroughly and moreover appreciate, a whole new branch of mathematics, Fuzzy Logic. Not just this but under his expert guidance and motivation, we could successfully come up with results we aimed to acquire since the beginning of this project. I am highly grateful to him for his strong support and understanding. I would also like to express my gratitude towards Dr. C.S. Sastry who helped us proving one of the two results.
before regulation. (c) First cluster formed after regulation. (B) Clustering. (a) Introduction of a novel data point with no left neighbor. (b) Creation of a new cluster before regulation. (c) Final appearance of the fuzzy partitioning after regulation. (d) Introduction of a novel data point with both left and right neighbors. (e) Creation of a new cluster before regulation. (f) Final appearance of the fuzzy partitioning after regulation (Tung et al., 2011).
Technically, big data analysis is analysis of data mining and techniques .Novel mining is the process of finding correlations or patterns among dozens of fields in large relational databases. Several types of analytical software are available: statistical, machine learning, and neural networks. As Novel contents keeps extending, the no. of pages crawled by the search engines is increases. With such large amount of data, estimating the relevant information satisfying the user query is a challenging task. Data prediction, Extraction and Alignment of big data from Novel databases is research area to obtain better mechanism and methodology to derive high precision and accuracy. Although many data extraction concepts such as ,  and  have proposed in literature related to research area but they still lag in some measurement regarding the data mining properties like precision and recall measures etc. Therefore, it’s a mandatory to ascertain the suitable solution for extraction and alignment of the big data. Another widespread application of Novel prediction is “personalization,” in which users are categorized based on their interests and tastes –. In Novel prediction and Extraction, we face challenges in preprocessing, clustering, classification and prediction. In existing works, , , prediction model based on fusing several prediction models like Markov and SVM models has been utilized, even it fails to reduce the false positive rate. This exploitation has enabled us to considerably improve the prediction accuracy. In this paper, we introduce an efficient framework for Novel Data extraction and clustering mechanism to user query obfuscations to alleviate the issue of scalability, ambiguity and precision in the number of query suggestions (prediction) and Query Result Records (QRR) as a clusters. In addition, the results indicate a dramatic improvement in prediction time for our objective. Moreover, the results demonstrate the positive effect of our proposed user specific clustering model in reducing the size of the prediction models through multi correlation factors
Our goal is to build an experimental framework for data streams similar to the WEKA framework, so that it will be easy for researchers to run experimental data stream bench- marks. New bagging methods were presented: ASHT Bag- ging using trees of different sizes, and ADWIN Bagging using a change detector to decide when to discard underperforming ensemble members. These methods compared favorably in a comprehensive cross-method comparison. Data stream eval- uation is fundamentally three-dimensional. These compar- isons, given your specific resource limitations, indicate the method of preference. For example, on the SEA Concepts and Forest Covertype datasets the best performing method across all three dimensions are arguably HT DDM and HT EDDM, as they are almost the fastest, and almost the most accurate and, by at least an order of magnitude, easily the most memory-efficient methods. On the other hand, if both runtime and memory consumption are less of a concern, then variants of bagging usually produce excellent accuracies.
Abstract: One of the most important functions of an export credit agency (ECA) is to act as an intermediary between national governments and exporters. These organizations provide financing to reduce the political and commercial risks in international trade. The agents assess the buyers based on financial and non-financial indicators to determine whether it is advisable to grant them credit. Because many of these indicators are qualitative and inherently linguistically ambiguous, the agents must make decisions in uncertain environments. Therefore, to make the most accurate decision possible, they often utilize fuzzyinference systems. The purpose of this research was to design a credit rating model in an uncertain environment using the fuzzyinferencesystem (FIS). In this research, we used suitable variables of agency ratings from previous studies and then screened them via the Delphi method. Finally, we created a credit rating model using these variables and FIS including related IF-THEN rules which can be applied in a practical setting.