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Clustering is a crucial method for deciphering data structure and producing new information. Due to its significance in revealing fundamental connections between the human brain and events, it is essential to utilize clustering for cognitive research. Dealing with noisy data caused by inaccurate synthesis from several sources or misleading data production processes is one of the most intriguing clustering difficulties. Noisy data can lead to incorrect object recognition and inference. This research aims to innovate a novel clustering approach, named Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering (PNTS3FCM), to solve the clustering problem with noisy data using neutral and refusal degrees in the definition of Picture Fuzzy Set (PFS) and Neutrosophic Set (NS). Our contribution is to propose a new optimization model with four essential components: clustering, outlier removal, safe semi-supervised fuzzy clustering and partitioning with labeled and unlabeled data. The effectiveness and flexibility of the proposed technique are estimated and compared with the state-of-art methods, standard Picture fuzzy clustering (FC-PFS) and Confidence-weighted safe semi-supervised clustering (CS3FCM) on benchmark UCI datasets. The experimental results show that our method is better at least 10/15 datasets than the compared methods in terms of clustering quality and computational time. [ABSTRACT FROM AUTHOR]

Transfer learning (TL) is further investigated in computer intelligence and artificial intelligence. Many TL methodologies have been suggested and applied to figure out the problem of practical applications, such as in natural language processing, classification models for COVID-19 disease, Alzheimer's disease detection, etc. FTL (fuzzy transfer learning) is an extension of TL that uses a fuzzy system to pertain to the vagueness and uncertainty parameters in TL, allowing the discovery of predicates and their evaluation of unclear data. Because of the system's increasing complexity, FTL is often utilized to further infer proper results without constructing the knowledge base and environment from scratch. Further, the uncertainty and vagueness in the daily data can arise and modify the process. It has been of great interest to design an FTL model that can handle the periodicity data with fast processing time and reasonable accuracy. This paper proposes a novel model to capture data related to periodical phenomena and enhance the quality of the existing inference process. The model performs knowledge transfer in the absence of reference or predictive information. An experimental stage on the UCI and real-life dataset compares our proposed model against the related methods regarding the number of rules, computing time, and accuracy. The experimental results validated the advantages and suitability of the proposed FTL model. [ABSTRACT FROM AUTHOR]

The detection of spatial and temporal changes (or change detection) in remote sensing images is essential in any decision support system about natural phenomena such as extreme weather conditions, climate change, and floods. In this paper, a new method is proposed to determine the inference process parameters of boundary point, rule coefficient, defuzzification coefficient, and dependency coefficient and present a new FWADAM+ method to train that set of parameters simultaneously. The initial data are clustered simultaneously according to each data group. This result will be the basis for determining a suitable set of parameters by using the FWADAM+ concurrent training algorithm. Eventually, these results will be inherited in the following data groups to build other complex fuzzy rule systems in a shorter time while still ensuring the model's efficiency. The weather imagery database of the United States Navy (US Navy) is used to evaluate and compare with some related methods using the root-mean-squared error (RMSE), R-squared (R2) measures, and the analysis of variance (ANOVA) model. The experimental results show that the proposed method is up to 30% better than the SeriesNet method, and the processing time is 10% less than that of the SeriesNet method. [ABSTRACT FROM AUTHOR]

This article proposes the Mamdani complex fuzzy inference system (Mamdani CFIS) to improve performance of the classical FIS and complex FIS. The applicability of the proposed CFIS is demonstrated by applying it to six commonly available datasets from UCI Machine Learning under the comparison with Mamdani FIS and the Adaptive Neuro Complex Fuzzy Inference System (ANCFIS). It is successfully proven that the proposed Mamdani CFIS is computationally less expensive and presents a more efficient method to handle time-series data and time-periodic phenomena, among all the fuzzy IS found thus far in the literature. Furthermore, the novelty of CFIS mainly lies in its implementation of the complex number throughout the entire procedures of computation. This gives much greater flexibility of implementing unexpected, nonlinear fluctuations. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new power-aware routing protocol for Wireless Sensor Network (WSN) based on the threshold rate and fuzzy logic for improving energy efficiency. The cluster heads are elected based on the probability values of every node in WSN, which are calculated from the remaining energy of every node. Cumulative remaining node energy is used to calculate mean energy of the whole network of the current phase. The nodes with high probability will have more chances to be selected as the cluster head, which gathers packets from the cluster member via single hop communication. The cluster head forwards the gathered data to sink by using fuzzy control with multi-hop communication. Fuzzy control takes three parameters namely queue length of a node, the distance of a node from the base station, and the remaining energy of node. The evidence from experiments suggest that the proposed energy efficient cluster-based routing protocol method (called MLSEEP) gives better results than the existing protocols by the supplement of those techniques. [ABSTRACT FROM AUTHOR]

Nowadays, colleges and universities focus on the assessment model for considering educational offers, suitable environments, and circumstances for students' growth, as well as the influence of Teaching Quality (TQ) and the applicability of the skills promoted by teaching to life. Teaching excellence is an important evaluation metric at the university level, but it is challenging to determine it accurately due to its wide range of influencing factors. Fuzzy and Deep Learning (DL) approaches must be could to build an assessment model that can precisely measure the teaching qualities to enhance accuracy. Combining fuzzy logic and DL can provide a powerful approach for assessing the influencing factors of college and university teaching effects by implementing the Sequential Intuitionistic Fuzzy (SIF) assisted Long Short-Term Memory (LSTM) model proposed. Sequential Intuitionistic Fuzzy (SIF) can be used sets to assess factors that affect teaching quality to enhance teaching methods and raise the standard of education. LSTM model to create a predictive model that can pinpoint the primary factors that influence teaching quality and forecast outcomes in the future using those influencing factors for academic growth. The enhancement of the SIF-LSTM model for assessing the influencing factors of teaching quality is proved by the accuracy of 98.4%, Mean Square Error (MSE) of 0.028%, Tucker Lewis Index (TLI) measure for all influencing factors and entropy measure of non-membership and membership degree correlation of factors related to quality in teaching by various dimensional measures. The effectiveness of the proposed model is validated by implementing data sources with a set of 60+ teachers' and students' open-ended questionnaire surveys from a university. [ABSTRACT FROM AUTHOR]

The Neutrosophic set (NS) has grasped concentration by its ability for handling indeterminate, uncertain, incomplete, and inconsistent information encountered in daily life. Recently, there have been various extensions of the NS, such as single valued neutrosophic sets (SVNSs), Interval neutrosophic sets (INSs), bipolar neutrosophic sets (BNSs), Refined Neutrosophic Sets (RNSs), and triangular fuzzy number neutrosophic set (TFNNs). This paper contains an extended overview of the concept of NS as well as several instances and extensions of this model that have been introduced in the last decade, and have had a significant impact in literature. Theoretical and mathematical properties of NS and their counterparts are discussed in this paper as well. Neutrosophic-set-driven decision making algorithms are also overviewed in detail. [ABSTRACT FROM AUTHOR]

Mobile ad hoc networks are non-dependent on any wired infrastructure and provide exceptional benefits during typical situations like conference, fair/exhibition, disaster recovery and emergency rescue operations. But these networks have to deal with several open issues, since they are battery operated and the dynamic topology can change due to mobility. Thus, multi-hop wireless ad hoc networking needs an energy efficient routing protocol that discovers/maintains routes to any other member node in the network, minimizing computation/communication overhead and battery power consumption. An adaptive neuro fuzzy inference system (ANFIS) based ad hoc routing approach has not been investigated before. In this paper, an ANFIS and cross layer approach-based energy efficient routing protocol for wireless multi-hop networks is proposed and its performance is compared with the Ad Hoc On-demand Distance Vector (AODV) Routing Protocol and fuzzy based AODV, using the network simulator ns2. Using remaining battery power, hop count and link stability as the input variables, the ANFIS module computes the optimum path cost, as the output. The proposed ANFIS based approach produces better results when compared using the metrics: average throughput, packet delivery ratio, average end-to-end delay in packet delivery between source and destination and average routing overhead. [ABSTRACT FROM AUTHOR]

A single-valued and an interval neutrosophic sets are two instances of the neutrosophic set(NS), which can efficiently deal with uncertain, imprecise and inconsistent information. In the present work, the work has been done under this environment to develop some novel hybrid aggregation operators based on arithmetic and geometric aggregation operators. The preferences related to the attributes are made in the form of single-valued and interval neutrosophic numbers. Their desirable properties such as idempotency, boundedness and monotonicity are also investigated. Furthermore, a decision-making approach presents to investigate the multi-criteria decision-making problem. The effectiveness of the approach is demonstrated through a case study and a comparison analysis with some other existing methods has been done to validate the results. [ABSTRACT FROM AUTHOR]

The decision-making problems based on fuzzy inference systems have received much attention from the worldwide scientific community. The M-CFIS-FKG model is considered one of the best models to solve classification problems based on uncertain and amplitude input datasets. It can infer and find the output labels of new samples that are not in the fuzzy rule base. Recently, the FKG-Pairs model has been considered an extension of FKG in the M-CFIS-FKG model by combining attribute pairs to infer and find the output labels in the cases of input datasets with incomplete gathering. It has overcome the limitation of the M-CFIS-FKG model. However, with real-time large input datasets or too-small training datasets, the FKG-Pairs model has also revealed limitations as it takes too much time to compute, and the accuracy is still relatively low. This paper has proposed a decision-making model in extreme cases (called FKG-Extreme) by using the Q-learning-based FKG-Pairs approach to enrich the fuzzy rule base after each time step with the cumulative mechanism of new rules. The proposed FKG-Extreme model has overcome the FKG-Pairs model's limitation in the extreme case. It has significantly improved the system's accuracy, while the computation time is acceptable. To validate the proposed model, we conducted experiments based on the standard UCI datasets, and the experimental results demonstrated that the system's performance in terms of accuracy is superior to the other reliable models in case of too small training data. Furthermore, the results of the two-way ANOVA also proved that the FKG-Extreme model is better than the FIS and FKG-Pairs models. [ABSTRACT FROM AUTHOR]

Traditional agricultural systems require huge amount of power for field watering. This paper proposes a smart irrigation system that helps farmers water their agricultural fields using Global System for Mobile Communication (GSM). This system provides acknowledgement messages about the job's statuses such as humidity level of soil, temperature of surrounding environment, and status of motor regarding main power supply or solar power. Fuzzy logic controller is used to compute input parameters (e.g. soil moisture, temperature and humidity) and to produce outputs of motor status. In addition, the system also switches off the motor to save the power when there is an availability of rain and also prevents the crop using panels from unconditional rain. The comparison is made between the proposed system, drip irrigation and manual flooding. The comparison results prove that water and power conservation are obtained through the proposed smart irrigation system. Image 1082 [ABSTRACT FROM AUTHOR]