Real-Time Prediction of Urban Traffic Problems Based on Artificial Intelligence-Enhanced Mobile Ad Hoc Networks(MANETS)

Traffic in today’s cities is a serious problem that increases travel times,negatively affects the environment,and drains financial resources.This study presents an Artificial Intelligence(AI)augmentedMobile Ad Hoc Networks(MANETs)based real-time prediction paradigm for urban traffic challenges.MANETs are wireless networks that are based on mobile devices and may self-organize.The distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion forecasts.This study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network(CSFPNN)technique to assess real-time data acquired from various sources within theMANETs.The framework uses the proposed approach to learn from the data and create predictionmodels to detect possible traffic problems and their severity in real time.Real-time traffic prediction allows for proactive actions like resource allocation,dynamic route advice,and traffic signal optimization to reduce congestion.The framework supports effective decision-making,decreases travel time,lowers fuel use,and enhances overall urban mobility by giving timely information to pedestrians,drivers,and urban planners.Extensive simulations and real-world datasets are used to test the proposed framework’s prediction accuracy,responsiveness,and scalability.Experimental results show that the suggested framework successfully anticipates urban traffic issues in real-time,enables proactive traffic management,and aids in creating smarter,more sustainable cities.

Double Vagueness: Uncertainty in Multi-scale Fuzzy Assignment of Duneness

In the automation of identification of landscape features the vaguenessarises from the fact that the attributes and parameters that make up a landscape vary over space andscale. In most of existing studies, these two kinds of vagueness are studied separately. This paperinvestigates their combination in identification of coast landscape units. Fuzzy set theory is usedto describe the vagueness of geomorphic features due to the continuity in space. The vaguenessresulted from the scale of measurement is evaluated by statistic indicators. The differences offuzzy objects derived from data at differing resolutions (in size from 3 X 3 cells to 25 X 25 cells)are studied in order to examine these higher-order uncertainties.

Analysis of Partial Volume Effects on Accurate Measurement of the Hippocampus Volume

Hippocampal volume loss is an important biomarker in distinguishing subjects with Alzheimer＇s disease （AD） and its measurement in magnetic resonance images （MRI） is influenced by partial volume effects （PVE）. This paper describes a post-processing approach to quantify PVE for correction of the hippocampal volume by using a spatial fuzzyC-means （SFCM） method. The algorithm is evaluated on a dataset of 20 T1-weighted MRI scans sampled at two different resolutions. The corrected volumes for left and right hippocampus （HC） which are 23% and 18% for the low resolution and 6% and 5% for the high resolution datasets, respectively are lower than hippocampal volume results from manual segmentation. Results show the importance of applying this technique in AD detection with low resolution datasets.

Risk Assessment of Heavy Metal Pollution in Sediments of the Fenghe River by the Fuzzy Synthetic Evaluation Model and Multivariate Statistical Methods

Concentrations of heavy metals in 74 sediment samples from the Fenghe River, which originates from the north of the Qinling Mountains and flows through Xi'an, Shaanxi Province, China, were characterized by employing geographic information system(GIS)mapping, fuzzy synthetic assessment, and multivariate statistical analysis to determine the enrichment characteristics of heavy metals as well as their potential risks of pollution to sediments. Al, Cd, and Co were the major pollutants, with a high enrichment factor(EF) value. Heavy metal concentrations from samples near the paper plant were maintained at a high level. Significant enrichment of Al, Ba, Cr, Ni, Pb, and Co was found in the midstream and downstream, while high concentration of Cu occurred in the headwater stream. Based on the cluster and principal component analyses, sediment metals mainly came from the paper plants, agronomic practices, natural sources, and tourism, with a contribution of 51.59%, 23.01%, 14.21%, and 9.88%, respectively. Sediment pollution assessment explored using fuzzy theory based on the entropy method and toxicity coefficient showed that 26, 32, and 11 sites fell into Class III(slightly polluted), Class IV(moderately polluted), and Class V(heavily polluted), respectively, and their scores of membership degree in the polluted level were on the rise, suggesting a relatively high degree of sediment metal pollution in the study area. Closely related to the excessive industrial and agricultural applications, metal pollution in sediment is necessary to be addressed in the Fenghe River.

A RVR-based Method for Bias Field Estimation in Brain Magnetic Resonance Images Segmentation

This paper presents a relevance vector regression(RVR) based on parametric approach to the bias field estimation in brain magnetic resonance(MR) image segmentation. Segmentation is a very important and challenging task in brain analysis,while the bias field existed in the images can significantly deteriorate the performance.Most of current parametric bias field correction techniques use a pre-set linear combination of low degree basis functions, the coefficients and the basis function types of which completely determine the field. The proposed RVR method can automatically determine the best combination for the bias field, resulting in a good segmentation in the presence of noise by combining with spatial constrained fuzzy C-means(SCFCM)segmentation. Experiments on simulated T1 images show the efficiency.