5G Data Offloading Using Fuzzification with Grasshopper Optimization Technique

Data offloading at the network with less time and reduced energy con-sumption are highly important for every technology.Smart applications process the data very quickly with less power consumption.As technology grows towards 5G communication architecture,identifying a solution for QoS in 5G through energy-efficient computing is important.In this proposed model,we perform data offloading at 5G using the fuzzification concept.Mobile IoT devices create tasks in the network and are offloaded in the cloud or mobile edge nodes based on energy consumption.Two base stations,small(SB)and macro(MB)stations,are initialized and thefirst tasks randomly computed.Then,the tasks are pro-cessed using a fuzzification algorithm to select SB or MB in the central server.The optimization is performed using a grasshopper algorithm for improving the QoS of the 5G network.The result is compared with existing algorithms and indi-cates that the proposed system improves the performance of the system with a cost of 44.64 J for computing 250 benchmark tasks.

A Similarity Based Fuzzy System as a Function Approximator

In this paper, an attempt is made to prove that some similarity based fuzzy systems can be found to behave as function approximators. A typical similarity based fuzzy system is proposed and its behaviour is shown to have the said property. It elucidates the connection between similarity relation and similarity measure of fuzzy sets to fuzzy inference methodology. The concept of similarity relation is used in fuzzification of crisp input values. Similarity index is used in measuring approximate equality of fuzzy sets over a given universe of discourse of a linguistic variable. The similarity between the observation(s) and the antecedent of a rule is used in selecting rule(s) for possible firing and also in modifying the relation between the antecedent and consequent of the rule based on the specific observation. Inference is drawn through the usual composition and subsequently by projecting the modified fuzzy restriction acting on the variables of interest on the universe of the linguistic variable in the consequent of the rule. A specificity based defuzzification scheme is proposed for multiple-rule firing. It has been proved systematically that such a similarity based fuzzy system can uniformly approximate continuous functions to any desired degree of accuracy on a closed and bounded interval. Simulation results are presented for the well-known dc-motor problem. A comparative study is made to establish the validity and efficiency of the proposed similarity based fuzzy system.

A Modification to the Fuzzy Regression Discontinuity Model to Settings with Fuzzy Variables

Despite the fact that fuzzy regression discontinuity designs are growing in popularity, a lot of research takes into account treatment non-compliance difficulties, specifically the fuzziness of the treatment impact. This paper took into account independent and dependent fuzzy factors when creating these designs. Additionally we took into account treatment non-compliance difficulties, specifically the fuzziness of the treatment impact, as other research does. The modified Fuzzy Regression Discontinuity model is preferable for modeling fuzzy data. It enables us to draw improved causal effects accommodating fuzzy variables, not just the fuzziness of the treatment effect as in Fuzzy Regression Discontinuity models. A fuzzy dataset is converted into crisp data by the Centroid method of defuzzification. Once the data is crisp, the traditional least squares methods of approximation are used to estimate the parameters in the model since these parameters are considered crisp whilst the error terms are fuzzy. The Alcohol Use Disorders Identification Test score(AUDIT score) can be used as a cutoff to initiate treatment in this case and can be used to predict the progression of HIV disease and/or AIDS. Counseling helps to lower the use of alcohol in people living with HIV/AIDS (PLWHA) as a result, improving the participants’ CD4 counts.

Investigation on the Effect of Geometrical and Geotechnical Parameters on Elongated Offshore Piles Using Fuzzy Inference Systems

Among numerous offshore structures used in oil extraction, jacket platforms are still the most favorable ones in shallow waters. In such structures, log piles are used to pin the substructure of the platform to the seabed. The pile＇s geometrical and geotechnical properties are considered as the main parameters in designing these structures. In this study, ANSYS was used as the FE modeling software to study the geometrical and geotechnical properties of the offshore piles and their effects on supporting jacket platforms. For this purpose, the FE analysis has been done to provide the preliminary data for the fuzzy-logic post-process. The resulting data were implemented to create Fuzzy Inference System （FIS） classifications. The resultant data of the sensitivity analysis suggested that the orientation degree is the main factor in the pile＇s geometrical behavior because piles which had the optimal operational degree of about 5° arc more sustained. Finally, the results showed that the related fuzzified data supported the FE model and provided an insight for extended offshore pile designs.

Textual Content Prediction via Fuzzy Attention Neural Network Model without Predefined Knowledge

Text analysis is a popular technique for finding the most significant information from texts including semantic,emotional,and other hidden features,which became a research hotspot in the last few years.Specially,there are some text analysis tasks with judgment reports,such as analyzing the criminal process and predicting prison terms.Traditional researches on text analysis are generally based on special feature selection and ontology model generation or require legal experts to provide external knowledge.All these methods require a lot of time and labor costs.Therefore,in this paper,we use textual data such as judgment reports creatively to perform prison term prediction without external legal knowledge.We propose a framework that combines value-based rules and a fuzzy text to predict the target prison term.The procedure in our framework includes information extraction,term fuzzification,and document vector regression.We carry out experiments with real-world judgment reports and compare our model’s performance with those of ten traditional classification and regression models and two deep learning models.The results show that our model achieves competitive results compared with other models as evaluated by the RMSE and R-squared metrics.Finally,we implement a prototype system with a user-friendly GUI that can be used to predict prison terms according to the legal text inputted by the user.

Water wave optimization-based routing protocol for vehicular adhoc networks

It is well known that Vehicular Adhoc Network(VANET)plays the major role in the field of Intelligent Transport System(ITS)to enhance road safety as well.The entire communication among vehicles is covered in VANETs.Since the number of researches is in progress for the enhancement of VANET model.Usually,the issues arising with this field is the achievement of multi-constrained Quality of Service(QoS)measures.To solve this issue,our paper intends to propose a cost model that considers certain network quality measures like travel,collision,congestion and the awareness about the QoS.Moreover,this paper adopts a renowned algorithm named Water Wave Optimization algorithm(WWO)to find the optimal route.The proposed method analyses its performance over other conventional methods like Lion Algorithm(LA),Genetic Algorithm(GA)and LA with Adaptive Mutation(LAAM)in terms of cost analysis,Convergence analysis and Computational time.Then the analytical results prove the superiority of proposed work in terms of decreased routing cost.