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Deep neural networks (DNN) have remarkably progressed in applications involving large and complex datasets but have been criticized as a black-box. This downside has recently become a motivation for the research community to pursue the ideas of hybrid approaches, resulting in novel hybrid systems classified as deep neuro-fuzzy systems (DNFS). Studies regarding the implementation of DNFS have rapidly increased in the domains of computing, healthcare, transportation, and finance with high interpretability and reasonable accuracy. However, relatively few survey studies have been found in the literature to provide a comprehensive insight into this domain. Therefore, this study aims to perform a systematic review to evaluate the current progress, trends, arising issues, research gaps, challenges, and future scope related to DNFS studies. A study mapping process was prepared to guide a systematic search for publications related to DNFS published between 2015 and 2020 using five established scientific directories. As a result, a total of 105 studies were identified and critically analyzed to address research questions with the objectives: (i) to understand the concept of DNFS; (ii) to find out DNFS optimization methods; (iii) to visualize the intensity of work carried out in DNFS domain; and (iv) to highlight DNFS application subjects and domains. We believe that this study provides up-to-date guidance for future research in the DNFS domain, allowing for more effective advancement in techniques and processes. The analysis made in this review proves that DNFS-based research is actively growing with a substantial implementation and application scope in the future.

Trust-based secure routing schemes are more effective than cryptographic routing protocols to convey energy-efficient data in WSNs since cryptographic protocols require high computation, more convergence time as well as storage space. The paper presents a well-organized trust estimation-based routing scheme (ETERS) that consists multi-trust (communication trust, energy trust, data trust) approach to alleviate several internal attacks like badmouthing, Sybil, selective forwarding, on–off,​ black hole, and gray-hole attacks for clustered WSN. The proposed multi-trust approach is used to analyze the credibility of sensitive monitored data. A novel and efficient cluster head selection algorithm (ECHSA) is employed to improve the performance of the cluster head (CH) selection process in clustered WSN. ECHSA allows the facility to elect a robust CH after a certain period to perform an equal load balance on all CHs. ETERS utilizes the Beta distribution-based trust function because recovery of trust values under attacks is faster in Beta distribution than Gaussian and Dirichlet distribution. ETERS also incorporates an irregular attenuation factor during the evaluation of communication trust to reflect the effect of various external factors such as natural calamity (earthquake), network congestion, etc., based on the trust values. However, a trust-based attack detection algorithm (TADA) assesses the reliability of SNs to detect internal attacks. TADA employs IDs, locations, and triple trust to detect internal attacks. Additionally, the proposed trust system employs an adjustable dynamic sliding length logical timing window to eradicate the limitations of existing trust models. Furthermore, a trust-based secure routing algorithm is incorporated to dynamically detect misbehavior in terms of packet forwarding for energy-efficient communication among sensor nodes. ETERS is compared with QEBSR, ATRP, ELPC, and SQEER to examine its performance. Experimental results on the MATLAB simulation platform exhibit excellent performance in terms of severity analysis, attack detection, and prevention to protect WSN, energy consumption under (i) normal (ii) attack scenario, latency (in sec.), packet delivery ratio (PDR), and throughput (in %) in the existence of spiteful nodes.

An investment portfolio implies the assortment of assets invested in the commodity market and equity funds across global markets. The critical issue associated with any portfolio under its optimization entails the achievement of an optimal Sharpe ratio related to risk-return. This issue turns complex when risk budgeting and other investor preferential constraints are weighed in, rendering it difficult for direct solving via conventional approaches. As such, this present study proposes a novel technique that addresses the problem of constrained risk budgeted optimization with multiple crossovers (binomial, exponential & arithmetic) together with the hall of fame via differential evolution (DE) strategies. The proposed automated solution facilitates portfolio managers to adopt the best possible portfolio that yields the most lucrative returns. In addition, the outcome coherence is verified by monitoring the best blend of evolution strategies. As a result, imminent outcomes were selected based on the best mixture of portfolio returns and Sharpe ratio. The monthly stock prices of Nifty50 were included in this study.

Fuzzy C-Means (FCM) is one of the mostly used techniques for fuzzy clustering and proven to be robust and more efficient based on various applications. Image segmentation, stock market and web analytics are examples of popular applications which use FCM. One limitation of FCM is that it only produces Gaussian membership function (MF). The literature shows that different types of membership functions may perform better than other types based on the data used. This means that, by only having Gaussian membership function as an option, it limits the capability of fuzzy systems to produce accurate outcomes. Hence, this paper presents a method to generate another popular shape of MF, the trapezoidal shape (trapMF) from FCM to allow more flexibility to FCM in producing outputs. The construction of trapMF is using mathematical theory of Gaussian distributions, confidence interval and inflection points. The cluster centers or mean (μ) and standard deviation (σ) from the Gaussian output are fully used to determine four trapezoidal parameters; lower limit a, upper limit d, lower support limit b, and upper support limit c with the assistance of function trapmf() in Matlab fuzzy toolbox. The result shows that the mathematical theory of Gaussian distributions can be applied to generate trapMF from FCM.

In the existing network-layered architectural stack of Cognitive Radio Ad Hoc Network (CRAHN), channel selection is performed at the Medium Access Control (MAC) layer. However, routing is done on the network layer. Due to this limitation, the Secondary/Unlicensed Users (SUs) need to access the channel information from the MAC layer whenever the channel switching event occurred during the data transmission. This issue delayed the channel selection process during the immediate routing decision for the channel switching event to continue the transmission. In this paper, a protocol is proposed to implement the channel selection decisions at the network layer during the routing process. The decision is based on past and expected future routing decisions of Primary Users (PUs). A learning agent operating in the cross-layer mode of the network-layered architectural stack is implemented in the spectrum mobility manager to pass the channel information to the network layer. This information is originated at the MAC layer. The channel selection is performed on the basis of reinforcement learning algorithms such as No-External Regret Learning, Q-Learning, and Learning Automata. This leads to minimizing the channel switching events and user interferences in the Reinforcement Learning- (RL-) based routing protocol. Simulations are conducted using Cognitive Radio Cognitive Network simulator based on Network Simulator (NS-2). The simulation results showed that the proposed routing protocol performed better than all the other comparative routing protocols in terms of number of channel switching events, average data rate, packet collision, packet loss, and end-to-end delay. The proposed routing protocol implies the improved Quality of Service (QoS) of the delay sensitive and real-time networks such as Cellular and Tele Vision (TV) networks.

The fatal disease of COVID-19 is wrapping up the whole world. Long-term lockdown policy has become the main reason for an increase in the unemployment rate all around the globe which has affected the economy of the developed and third world countries disastrously. This paper is written to suggest a solution for the COVID-19 pandemic. The best counter-strategy would be to clean a region with a preplanned structure of short-term lockdown using the Strategic Algorithm. The area needs to be divided into two equal parts and each part will then be further divided into five subparts. These parts will be cleaned and sanitized simultaneously through the specific set of rules determined by the Strategic Algorithm. Results of the cleaning mechanism are generated using simulation of non-deterministic and deterministic automata for each accepted or cleaned area in the JFLAP tool. The current study provides a short-term lockdown mechanism for cleaning a specific area and taking other strategic steps within the lockdown period to minimize the COVID-19 effects.

This study aims to determine the design stages of developing blended learning smartphone media learning products and to determine the feasibility of integral learning media using the developed flipbook. This study uses a small-scale trial on FHIP UHAMKA while for large-scale trials conducted by Malaysian UTP with 29 respondents including Physics Education FKIP UHAMKA, Mathematics Education FKIP UHAMKA and also Malaysian UTP. The method used is Research and Development (R&D) using the Brog and Gall development procedure. The results of the media interface test obtained a percentage of 77.18%. This shows that the products developed included in the category are very feasible to use while for the effectiveness of students as teaching material in the learning process, the average of the effectiveness of students is 83.26%, the value obtained can be said to be very well seen from the Likert scale index used, then from the assessment of the effectiveness of media students it is feasible to be used in integral learning. Blended learning media with character given to one class in FKIP UHAMKA, obtained an average post-test score of 48.04. The data obtained, the character development of the character questionnaire was 75.04.

Society 5.0 is associated not only with technological advances, but doing system updates in their respective fields. Respondents obtained were 37 in Universiti Teknologi Petronas (UTP). This study aims to analyze the effectiveness of character value in blended learning. The research model used is a quantitative method with a frequency histogram based on the Likert scale from (1 never to 5 always). This paper describes how to see the connection between one character value indicator and another. This result imply that the character analysis value in blended learning methods in teaching has received positive feedback from students, the average mean (responsibility = 81.08; independently = 85.63; discipline = 74.86; curiosity = 74.32; cooperation = 85.09).

Compliance monitoring for quality of web service (QoWS) has accuracy issues due to uncertain network behaviors. Existing models use precise computation-based methods for defining and monitoring QoWS requirements, but these methods have limited ability to handle uncertainties. Consequently, the accuracy of the monitoring results is degraded. Defining expected QoWS using exact values is unrealistic, as generally not all service requestors know what values should be specified in the contract. Therefore, this paper proposes an interval type-2 (IT2) fuzzy model for QoWS compliance monitoring because it has greater capability than precise computation methods to reduce the effects of uncertainties. IT2 also has greater capability than the traditional fuzzy sets to manage uncertainty problem due to its non-crisp membership degrees assigned to the input. The model is able to perform compliance monitoring on linguistically defined QoWS. The model is developed based on fuzzy C-means algorithm, and the number of clusters is optimized using a clustering validity index. The model is constructed based on a Mamdani fuzzy inference system. The results show that the IT2 model outperforms type-1 fuzzy and precise computation-based models in terms of the accuracy of monitoring results. This research results in more accurate and precise QoWS compliance monitoring. It also provides user-centric QoWS specifications because requestors can define their requirements using linguistic values.

Fuzzy logic is an algorithm that works on “degree of truth”, instead of the conventional crisp logic where the possible answer can be 1 or 0. Fuzzy logic resembles human thinking as it considers all the possible outcomes between 1 and 0 and it tries to reflect reality. Generation of membership functions is the key factor of fuzzy logic. An approach for generating fuzzy gaussian and triangular membership function using fuzzy c-means is considered in this research. The problem related to sunspot prediction is considered and its accuracy is calculated. It is evident from the results that the proposed technique of generating membership functions using fuzzy c-means can be adopted for predicting sunspots.

Techniques involved artificial intelligence and machine learning offers various classification methods in order to deal with daily life problems. Among these methods, Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Deep Neural Network (DNN) are the most commonly used classifiers. Since ANFIS is not suitable for high-dimensional data, therefore DNN was introduced to overcome this problem faced by conventional methods. However, due to the optimization of millions of parameters in their deep architecture, the decision made by DNN faced the criticism of being non-transparent. To overcome this problem, recently, various researchers are coming up with the idea of using fuzzy logic with DNN. Therefore, this study also proposed a Deep Neuro-Fuzzy Classifier (DNFC) with a cooperative based structure for solving classification problems, particularly. The performance of the proposed DNFC was evaluated with ANFIS and DNN classifier, where overall results show that the performance of ANFIS classifier decreased when input size increased. While the performance of the proposed model demonstrated nearly similar or slightly higher accuracy as compared to DNN.

In the traditional hypotheses test, hypotheses are crisp. In this paper, we consider the hypotheses test for unknown mean in normal populations with fuzzy data when the standard deviation of the population is known. This paper aims to distinguish various parameter effects on clinical Heart Patients with Two-way Anova, and in this fuzzy test, we will make a fuzzy decision for rejection or acceptance null hypothesis on various parameters of clinical data of Heart Patients with Fuzzy p-value and compared the results with the conventional hypothesis test results. These results will be a benchmark for new patients (same characteristics as the old patients) to treat them in a better way.

Fuzzy logic is a way of many-valued computing logic that deals with the truth values of the variables between 0 and 1, unlike the conventional Boolean logic. Membership functions are used to depict the fuzzy values of given variable. Though membership functions are determined through expert’s opinion, however, the one estimated through heuristic algorithms is the preferable methods. Membership functions determined through statistical and knowledge engineering methods are usually application dependent and cannot be applied on different datasets. This research focuses on generating the parametric values of the triangular membership function using a novel method. Initially, the Fuzzy C-means algorithm is utilized to generate the parameters values of the Gaussian membership function. Using a set of equations, these values then estimate the parameters of the triangular membership function. The proposed method is applied to the quality of web services data. From the results it is verified that the new approach of generating triangular membership functions can be adopted.

Data-driven predictive maintenance for the prediction of machine failure has been widely studied and performed to test machine failures. Predictive maintenance refers to the machine learning method, which utilizes data for identification of potential system malfunction and provides an alert when a system assessed to be prone to breakdown. The proposed work reveals a novel framework called Artificial Intelligence Monitoring 4.0 (AIM 4.0), which is capable of determining the current condition of equipment and provide a predicted mean time before failure occurs. AIM 4.0 utilizes three different ensemble machine learning methods, including Gradient Boost Machine (GBM), Light GBM, and XGBoost for prediction of machine failures. The machine learning methods stated are implemented to produce acceptable accuracy for the monitoring task as well as producing a prediction with a high confidence level.

Sea has always played a pivotal role in human life. It formulates the weather, provides transportation medium, food, natural resources like oil and gas, and much more. Countless commercial and industrial activities take place on the surface of the sea, thus understanding, classifying and predicting the sea surface wave is a topic of great interest. Many numerical models (NM) have been proposed to model the behavior of sea waves, however, they are complex and costly for site-specific studies. On the other hand, data-driven machine learning (ML) models have recently proved to be an effective solution for site-specific classification, real-time or near-future prediction problems. The ML approach utilizes marine datasets to train, test and validate the model. In this paper, we present a survey of ML studies on the topic of classification and prediction of sea wave parameters. We hope that this paper provides a holistic model-based view to new researchers and pave the path for future research.

This paper proposes a hybrid prediction technique based on Recurrent Neural Network Long-Short-Term Memory (RNN-LSTM) with the integration of Particle Swarm Optimization (PSO) algorithm to estimate the Remaining Useful Life (RUL) of machines. LSTM is an improvement of RNN as RNN faces issues with predicting long-term dependencies. Issues such as vanishing and exploding gradients are the results of backpropagating errors, taking place when the network is learning to store and relate information over extended time intervals. RNN-LSTM is a feasible technique for this research due to its effectiveness in resolving sequential long-term dependencies problems. However, the accuracy can still be enhanced to a satisfactory value considering the optimal network topology has not been discovered yet. Accuracy improvement can be achieved by resorting to hyperparameter tuning. A result of proof of concept validates that by increasing the number of epochs, the accuracy of prediction has improved but increases the execution time. To optimize between the accuracy and execution time, a population-inspired Particle Swarm Optimization (PSO) algorithm is employed. PSO will be utilized to select the optimal RNN-LSTM topology specifically the learning rate instead of using manual search. This optimized hybrid prediction technique is useful to be implemented in predictive maintenance to predict machine failure.

Many algorithms have been developed to reduce the motion artifact effect on Photoplethysmograph (PPG) technology and to increase the accuracy of the health monitoring device reading. It is found that existing solutions are still lacking in getting high accuracy of heart rate reading. Therefore, we propose a research to formulate an improved motion artifact reduction approach using variable step-size least mean square (VSSLMS). The objective of this paper is to review VSSLMS for motion artifact reduction. A total of eight manuscripts, collected from ISI, Scopus and Google Scholar indexing databases, were critically reviewed. The review revealed that VSSLMS is better than LMS in reducing the motion artifact in slow motion and high-speed motion. For future work, the VSSLMS results will be formulated with regression machine learning.

Clustering is more popular than the expert knowledge approach in Interval Fuzzy Type-2 membership function construction because it can construct membership function automatically with less time consumption. Most research proposed a two-fuzzifier fuzzy C-Means clustering method to construct Interval Fuzzy Type-2 membership function which mainly focused on producing Gaussian membership function. The other two important membership functions, triangular and trapezoidal, are constructed using the grid partitioning method. However, the method suffers a drawback of not being able to represent actual data composition in the underlying dataset. Some research proposed triangular and trapezoidal membership functions construction using readily formed Fuzzy Type-1 membership functions, which means it remains unclear how the membership functions are heuristically constructed using fuzzy C-Means outputs. The triangular and trapezoidal membership functions are important because previous works have shown that they may produce superior performance than Gaussian membership function in some applications. Therefore, this paper presents a structured literature review on generating triangular and trapezoidal Interval Fuzzy Type-2 membership functions using fuzzy C-Means. Initially, 110 related manuscripts were collected from Web of Science, Scopus, and Google Scholar. These manuscripts went through the identification, screening, eligibility, and inclusion processes, and as a result, 21 manuscripts were reviewed and discussed in this paper. To ensure that the review also covers the important components of fuzzy logic, this paper also reviews and discusses another 49 manuscripts on fuzzy calculation and operation. Furthermore, this paper also discusses the contributions of the conducted review to the body of knowledge, future research directions and challenges, with the aim to motivate the future works of constructing the methods to generate Interval Fuzzy Type-2 triangular and trapezoidal membership functions using fuzzy C-Means. The methods imply flexibility in choosing membership function type, hence increasing the effectiveness of fuzzy applications through leveraging the advantages that each of the three membership function types could provide.

Data Streams create new challenges for fuzzy clustering algorithms, specifically Interval Type-2 Fuzzy C-Means (IT2FCM). One problem associated with IT2FCM is that it tends to be sensitive to initialization conditions and therefore, fails to return global optima. This problem has been addressed by optimizing IT2FCM using Ant Colony Optimization approach. However, IT2FCM-ACO obtain clusters for the whole dataset which is not suitable for clustering large streaming datasets that may be coming continuously and evolves with time. Thus, the clusters generated will also evolve with time. Additionally, the incoming data may not be available in memory all at once because of its size. Therefore, to encounter the challenges of a large data stream environment we propose improvising IT2FCM-ACO to generate clusters incrementally. The proposed algorithm produces clusters by determining appropriate cluster centers on a certain percentage of available datasets and then the obtained cluster centroids are combined with new incoming data points to generate another set of cluster centers. The process continues until all the data are scanned. The previous data points are released from memory which reduces time and space complexity. Thus, the proposed incremental method produces data partitions comparable to IT2FCM-ACO. The performance of the proposed method is evaluated on large real-life datasets. The results obtained from several fuzzy cluster validity index measures show the enhanced performance of the proposed method over other clustering algorithms. The proposed algorithm also improves upon the run time and produces excellent speed-ups for all datasets.

The growing number of cloud computing providers has made the selection process difficult and time consuming. This motivates many research to propose cloud computing recommendation system. However, generating an overall rating of a provider based on accumulation of several performance parameters involves uncertainties especially when the computation comprises network-related performance parameters. Hence, this paper proposes a fuzzy logic-based model that evaluates providers based on downlink, uplink and latency parameters, and determines the best provider. The model was constructed based on historical data of the real cloud computing providers implementation. The model was compared against non-fuzzy model, and it showed that the proposed fuzzy logic-based model is better in terms of accuracy. This work implies faster and accurate selection of cloud computing provider.

Inappropriate team composition is one of the important factors that can impact the overall process of software development. Numerous models for team composition have also been suggested, yet they have been disapproved by the researchers and organisations for having ineffectiveness in yielding positive results. Therefore, this study proposes a rough-fuzzy model for selecting team leader for software development teams to avoid the limitations of individual techniques (i.e., Rough Set Theory (RST) or Fuzzy Set Theory (FST)). Moreover, the model development was divided into two portions: Decision Rules Development and Fuzzy Inference System (FIS) development. Johnson Algorithm (JA) was applied using ROSETTA toolkit under rough set theory principles for decision rule construction. Decision rules were then used under Mamdani’s fuzzy inference method. At the end, the developed model was validated based on the results of prediction accuracy and F1-measures.

There are existing implementations of Latent Dirichlet Allocation (LDA) algorithm as a semantic classifier to arrange the data for efficient retrieval. However, the problem of learning or inferencing the posterior distribution of the algorithm is trivial. Inferencing directly the prior distribution could lead to time taken to increase exponentially. It is due to the coupling of the hyperparameters. Several inference algorithms have been implemented together with LDA to solve this issue. The inference algorithm used in this research work is Gibbs sampling. Research using Gibbs sampling shows promising results in comparison to other inference algorithms, especially in the performance of the algorithm. It still takes a long time to compute the topic distribution of the data. There are still room for improvement in the time taken for the algorithm to complete the topic distribution. Using two datasets, an evaluation of the performance of the algorithm has been conducted. Results show that Gibbs sampling as the inference algorithm provides a better prediction on the optimal number of topic of the data in comparison to Variational Expectation Maximization (VEM).

The aim of this research is to develop constructs for a measurement to assess children's emotional connection towards technology. The emerging technologies impact our way of livings in different ways regardless of age. If technology is properly used, it can make betterment to one's life, but if it is improperly used, it causes severity to human beings. The impact of technology that we may not realize is related to young adult especially underage children. The main problem identified is the excessive usage of technology can impact children negatively. It is a real life problem because firstly, parents have no idea what level of screen time is appropriate for children and secondly, the children's interaction with technology is inadequately control. The impact arises from the excessive usage of technology could be hindered if we could be informed about the severity of technology usage among children. Therefore the objective of this article is to define technology usage and emotional connection from reviewing literature. The technology being considered is mobile devices such as smartphones and tablet. Findings from the literature reviewed suggested that there is a relationship between technology use and emotional connection as spending too much time in front of screens could lead to an emotional dependency.