Study on Sentiment Classification Strategies Based on the Fuzzy Logic with Crow Search Algorithm

In recent times, sentiment analysis research has gained wide popularity. That situation is caused by the nature of online applications that allow users to express their opinions on events, services, or products through social media applications such as Twitter, Facebook, and Amazon. This paper proposes a novel sentiment classification method according to the Fuzzy rule-based system (FRBS) with crow search algorithm (CSA). FRBS is used to classify the polarity of sentences or documents, and the CSA is employed to optimize the best output from the fuzzy logic algorithm. The FRBS is applied to extract the sentiment and classify its polarity into negative, neutral, and positive. Sometimes, the outputs of the FRBS must be enhanced, especially since many variables are present and the rules between them overlap. For such cases, the CSA is used to solve this limitation faced by FRBS to optimize the outputs of FRBS and achieve the best result. We compared the performance of our proposed model with different machine learning algorithms, such as SVM, maximum entropy, boosting, and SWESA. We tested our model on three famous data sets collected from Amazon, Yelp, and IMDB. Experimental results demonstrated the effectiveness of the proposed model and achieved competitive performance in terms of accuracy, recall, precision, and the F–score.

Integration of fuzzy logic in the SEIRD compartmental model for the analysis of intervention and transmission heterogeneity on SARS-CoV-2 transmission dynamics

Different epidemiological compartmental models have been presented to predict the transmission dynamics of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this study, we have proposed a fuzzy rule-based Susceptible-Exposed-Infectious-Recovered-Death ([Formula: see text]) compartmental model considering a new dynamic transmission possibility variable as a function of time and three different fuzzy linguistic intervention variables to delineate the intervention and transmission heterogeneity on SARS-CoV-2 viral infection. We have analyzed the datasets of active cases and total death cases of China and Bangladesh. Using our model, we have predicted active cases and total death cases for China and Bangladesh. We further presented the correspondence of different intervention measures in relaxing the transmission possibility. The proposed model delineates the correspondence between the intervention measures as fuzzy subsets and the predicted active cases and total death cases. The prediction made by our system fitted the collected dataset very well while considering different fuzzy intervention measures. The integration of fuzzy logic in the classical compartmental model also produces more realistic results as it generates a dynamic transmission possibility variable. The proposed model could be used to control the transmission of SARS-CoV-2 as it deals with the intervention and transmission heterogeneity on SARS-CoV-2 transmission dynamics.

A Theoretical Linguistic Fuzzy Rule-Based Compartmental Modeling for the COVID-19 Pandemic

Recently COVID-19 pandemic has affected the whole world quite seriously. The number of new infectious cases and death cases are rapidly increasing over time. In this study, a theoretical linguistic fuzzy rule-based Susceptible-Exposed-Infectious-Isolated-Recovered (SEIIsR) compartmental model has been proposed to predict the dynamics of the transmission of COVID-19 over time considering population immunity and infectiousness heterogeneity based on viral load in the model. The model’s equilibrium points have been calculated and stability analysis of the model’s equilibrium points has been conducted. Consequently, the fuzzy basic reproduction number, R0f of the fuzzy model has been formulated. Finally, the temporal dynamics of different compartmental populations with immunity and infectiousness heterogeneity using the fuzzy Mamdani model are delineated and some disease control policies have been suggested to get over the infection in no time.

Query-focused multi-document text summarization using fuzzy inference

The present paper proposes a fuzzy inference system for query-focused multi-document text summarization (MTS). The overall scheme is based on Mamdani Inferencing scheme which helps in designing Fuzzy Rule base for inferencing about the decision variable from a set of antecedent variables. The antecedent variables chosen for the task are from linguistic and positional heuristics, and similarity of the documents with the user-defined query. The decision variable is the rank of the sentences as decided by the rules. The final summary is generated by solving an Integer Linear Programming problem. For abstraction coreference resolution is applied on the input sentences in the pre-processing step. Although designed on the basis of a small set of antecedent variables the results are very promising.

Fuzzy Logic-Based Controller for Bipedal Robot

In this paper, the problem of controlling a human-like bipedal robot while walking is studied. The control method commonly applied when controlling robots in general and bipedal robots in particular, was based on a dynamical model. This led to the need to accurately define the dynamical model of the robot. The activities of bipedal robots to replace humans, serve humans, or interact with humans are diverse and ever-changing. Accurate determination of the dynamical model of the robot is difficult because it is difficult to fully and accurately determine the dynamical quantities in the differential equations of motion of the robot. Additionally, another difficulty is that because the robot’s operation is always changing, the dynamical quantities also change. There have been a number of works applying fuzzy logic-based controllers and neural networks to control bipedal robots. These methods can overcome to some extent the uncertainties mentioned above. However, it is a challenge to build appropriate rule systems that ensure the control quality as well as the controller’s ability to perform easily and flexibly. In this paper, a method for building a fuzzy rule system suitable for bipedal robot control is proposed. The design of the motion trajectory for the robot according to the human gait and the analysis of dynamical factors affecting the equilibrium condition and the tracking trajectory were performed to provide informational data as well as parameters. Based on that, a fuzzy rule system and fuzzy controller was proposed and built, allowing a determination of the control force/moment without relying on the dynamical model of the robot. For evaluation, an exact controller based on the assumption of an accurate dynamical model, which was a two-feedback loop controller based on integrated inverse dynamics with proportional integral derivative, is also proposed. To confirm the validity of the proposed fuzzy rule system and fuzzy controller, computation and numerical simulation were performed for both types of controllers. Comparison of numerical simulation results showed that the fuzzy rule system and the fuzzy controller worked well. The proposed fuzzy rule system is simple and easy to apply.

A Fuzzy Rule-Based Fog Cloud Computing untuk Menganalisis Faktor Penyebab Kematian Ibu Meninggal Masa Postpartum

Analisis penyebab kematian ibu pasca postpartum sangatlah penting dalam mengurang kematian ibu. Angka kematian ibu pasca postpartum diindonesia sangatlah tinggi sekitar 359 dalam 100.000 kelahiran. Faktor -faktor kematian ibu pasca postpartum jarang dianalisis lebih dalam sehingga tidak ada penanganan berkelanjuttan. Pada penelitian ini penulis menggunakan system fuzzy rule-based fog cloud computing untuk memonitoring kematian ibu berdasarkan tanda-tanda vital dan tanda-tanda fisik dengan menggunakan 10 parameter penyebab kematian ibu masa postpartum. Fog cloud digunakan untuk pengolahaan data dalam sever local agar mengurangi beban saat menggunakan cloud computing. Analisis awal parameter kematian ibu dirubah kedalam rentang low, medium dan high kemudian dimonitoring pada tujuh hari pasca postpartum dari hasil analisis yang dilakukan didapat faktor dominan kematian ibu pasca postpartum untuk tanda-tanda vital dipengaruhi oleh Blood Pressure dengan nilai MF 0,44 dengan status “High” untuk tanda-tanda fisik dipengaruhi oleh Anogenital Lochea Sanguinolenta dengan nilai MF 0,46 dengan status dalam system “High”. Parameter lain tidak terlalu berpengaruh terhadap kematian ibu cenderung turun dari hari pertama hingga hari ke tujuh pasca postpartum.