Driving Style Recognition System Using Smartphone Sensors Based on Fuzzy Logic

Every 24 seconds,someone dies on the road due to road accidents and it is the 8th leading cause of death and the first among children aged 15–29 years.1.35 million people globally die every year due to road traffic crashes.An additional 20–50 million suffer from non-fatal injuries,often resulting in longterm disabilities.This costs around 3%of Gross Domestic Product to most countries,and it is a considerable economic loss.The governments have taken various measures such as better road infrastructures and strict enforcement of motor-vehicle laws to reduce these accidents.However,there is still no remarkable reduction in the number of accidents.To ensure driver safety and achieve vision of zero accidents,there is a great need to monitor drivers’driving styles.Most of the existing driving behavior monitoring solutions are based on expensive hardware sensors.As most people are using smartphones in the modern era,a system based on mobile application is proposed,which can reduce the cost for developing intelligent transport systems(ITS)to a large extent.In this paper,we utilize the accelerometer sensor data and the global positioning system(GPS)sensor deployed in smartphones to recognize driving and speeding events.A driving style recognition system based on fuzzy logic is designed to classify different driving styles and control reckless driving by taking the longitudinal/lateral acceleration and speed as input parameters.Thus,the proposed system uses fuzzy logic rather than taking the crisp values of the sensors.Results indicate that the proposed system can classify reckless driving based on fuzzy logic and,therefore,reduce the number of accidents.

Autism Spectrum Disorder Prediction by an Explainable Deep Learning Approach

Autism Spectrum Disorder (ASD) is a developmental disorderwhose symptoms become noticeable in early years of the age though it canbe present in any age group. ASD is a mental disorder which affects the communicational, social and non-verbal behaviors. It cannot be cured completelybut can be reduced if detected early. An early diagnosis is hampered by thevariation and severity of ASD symptoms as well as having symptoms commonly seen in other mental disorders as well. Nowadays, with the emergenceof deep learning approaches in various fields, medical experts can be assistedin early diagnosis of ASD. It is very difficult for a practitioner to identifyand concentrate on the major feature’s leading to the accurate prediction ofthe ASD and this arises the need for having an automated approach. Also,presence of different symptoms of ASD traits amongst toddlers directs tothe creation of a large feature dataset. In this study, we propose a hybridapproach comprising of both, deep learning and Explainable Artificial Intelligence (XAI) to find the most contributing features for the early and preciseprediction of ASD. The proposed framework gives more accurate predictionalong with the recommendations of predicted results which will be a vital aidclinically for better and early prediction of ASD traits amongst toddlers.

Obtaining Crisp Priorities for Triangular and Trapezoidal Fuzzy Judgments

This paper proposes anoptimal fuzzy-based model for obtaining crisp priorities for Fuzzy-AHP comparison matrices.Crisp judgments cannot be given for real-life situations,as most of these include some level of fuzziness and com-plexity.In these situations,judgments are represented by the set of fuzzy numbers.Most of the fuzzy optimization models derive crisp priorities for judgments repre-sented with Triangular Fuzzy Numbers(TFNs)only.They do not work for other types of Triangular Shaped Fuzzy Numbers(TSFNs)and Trapezoidal Fuzzy Numbers(TrFNs).To overcome this problem,a sum of squared error(SSE)based optimization model is proposed.Unlike some other methods,the proposed model derives crisp weights from all of the above-mentioned fuzzy judgments.A fuzzy number is simulated using the Monte Carlo method.A threshold-based constraint is also applied to minimize the deviation from the initial judgments.Genetic Algorithm(GA)is used to solve the optimization model.We have also conducted casestudiesto show the proposed approach’s advantages over the existingmethods.Results show that the proposed model outperforms other models to minimize SSE and deviation from initial judgments.Thus,the proposed model can be applied in various real time scenarios as it can reduce the SSE value upto 29%compared to the existing studies.

Multi-dimensional information-driven many-objective software remodularization approach

Most of the search-based software remodularization(SBSR)approaches designed to address the software remodularization problem(SRP)areutilizing only structural information-based coupling and cohesion quality criteria.However,in practice apart from these quality criteria,there require other aspects of coupling and cohesion quality criteria such as lexical and changed-history in designing the modules of the software systems.Therefore,consideration of limited aspects of software information in the SBSR may generate a sub-optimal modularization solution.Additionally,such modularization can be good from the quality metrics perspective but may not be acceptable to the developers.To produce a remodularization solution acceptable from both quality metrics and developers’perspectives,this paper exploited more dimensions of software information to define the quality criteria as modularization objectives.Further,these objectives are simultaneously optimized using a tailored manyobjective artificial bee colony(MaABC)to produce a remodularization solution.To assess the effectiveness of the proposed approach,we applied it over five software projects.The obtained remodularization solutions are evaluated with the software quality metrics and developers view of remodularization.Results demonstrate that the proposed software remodularization is an effective approach for generating good quality modularization solutions.