Fuzzy Difference Equations in Diagnoses of Glaucoma from Retinal Images Using Deep Learning

The intuitive fuzzy set has found important application in decision-making and machine learning.To enrich and utilize the intuitive fuzzy set,this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images converge.Retinal image detections are categorized as normal eye recognition,suspected glaucomatous eye recognition,and glaucomatous eye recognition.Fuzzy degrees associated with weighted values are calculated to determine the level of concentration between the fuzzy partition and the retinal images.The proposed model was used to diagnose glaucoma using retinal images and involved utilizing the Convolutional Neural Network(CNN)and deep learning to identify the fuzzy weighted regularization between images.This methodology was used to clarify the input images and make them adequate for the process of glaucoma detection.The objective of this study was to propose a novel approach to the early diagnosis of glaucoma using the Fuzzy Expert System(FES)and Fuzzy differential equation(FDE).The intensities of the different regions in the images and their respective peak levels were determined.Once the peak regions were identified,the recurrence relationships among those peaks were then measured.Image partitioning was done due to varying degrees of similar and dissimilar concentrations in the image.Similar and dissimilar concentration levels and spatial frequency generated a threshold image from the combined fuzzy matrix and FDE.This distinguished between a normal and abnormal eye condition,thus detecting patients with glaucomatous eyes.

Progress in doping and crystal deformation for polyanions cathode based lithium-ion batteries

Polyanion-based materials are considered one of the most attractive and promising cathode materials for lithiumion batteries(LIBs)due to their good stability,safety,cost-effectiveness,suitable voltages,and minimal environmental impact.However,these materials suffer from poor rate capability and low-temperature performance owing to limited electronic and ionic conductivity,which restricts their practical applicability.Recent developments,such as coating material particles with carbon or a conductive polymer,crystal deformation through the doping of foreign metal ions,and the production of nanostructured materials,have significantly enhanced the electrochemical performances of these materials.The successful applications of polyanion-based materials,especially in lithium-ion batteries,have been extensively reported.This comprehensive review discusses the current progress in crystal deformation in polyanion-based cathode materials,including phosphates,fluorophosphates,pyrophosphates,borates,silicates,sulfates,fluorosilicates,and oxalates.Therefore,this review provides detailed discussions on their synthesis strategies,electrochemical performance,and the doping of various ions.

Response of fuzzy clustering on different threshold determination algorithms in spectral change vector analysis over Western Himalaya, India

Abstract： Change detection is a standard tool to extract and analyze the earth＇s surface features from remotely sensed data. Among the different change detection techniques, change vector analysis （CVA） have an exceptional advantage of discriminating change in terms of change magnitude and vector direction from multispectral bands. The estimation of precise threshold is one of the most crucial task in CVA to separate the change pixels from unchanged pixels because overall assessment of change detection method is highly dependent on selected threshold value. In recent years, integration of fuzzy clustering and remotely sensed data have become appropriate and realistic choice for change detection applications. The novelty of the proposed model lies within use of fuzzy maximum likelihood classification （FMLC） as fuzzy clustering in CVA. The FMLC based CVA is implemented using diverse threshold determination algorithms such as double-window flexible pace search （DFPS）, interactive trial and error （T＆E）, and 3x3-pixel kernel window （PKW）. Unlike existing CVA techniques, addition of fuzzy clustering in CVA permits each pixel to have multiple class categories and offers ease in threshold determination process. In present work, the comparative analysis has highlighted the performance of FMLC based CVA overimproved SCVA both in terms of accuracy assessment and operational complexity. Among all the examined threshold searching algorithms, FMLC based CVA using DFPS algorithm is found to be the most efficient method.

Packet Optimization of Software Defined Network Using Lion Optimization

There has been an explosion of cloud services as organizations take advantage of their continuity,predictability,as well as quality of service and it raises the concern about latency,energy-efficiency,and security.This increase in demand requires new configurations of networks,products,and service operators.For this purpose,the software-defined network is an efficient technology that enables to support the future network functions along with the intelligent applications and packet optimization.This work analyzes the offline cloud scenario in which machines are efficiently deployed and scheduled for user processing requests.Performance is evaluated in terms of reducing bandwidth,task execution times and latencies,and increasing throughput.A minimum execution time algorithm is used to compute the completion time of all the available resources which are allocated to the virtual machine and lion optimization algorithm is applied to packets in a cloud environment.The proposed work is shown to improve the throughput and latency rate.

Software Reliability Assessment Using Hybrid Neuro-Fuzzy Model

Software reliability is the primary concern of software developmentorganizations, and the exponentially increasing demand for reliable softwarerequires modeling techniques to be developed in the present era. Small unnoticeable drifts in the software can culminate into a disaster. Early removal of theseerrors helps the organization improve and enhance the software’s reliability andsave money, time, and effort. Many soft computing techniques are available toget solutions for critical problems but selecting the appropriate technique is abig challenge. This paper proposed an efficient algorithm that can be used forthe prediction of software reliability. The proposed algorithm is implementedusing a hybrid approach named Neuro-Fuzzy Inference System and has also beenapplied to test data. In this work, a comparison among different techniques of softcomputing has been performed. After testing and training the real time data withthe reliability prediction in terms of mean relative error and mean absolute relativeerror as 0.0060 and 0.0121, respectively, the claim has been verified. The resultsclaim that the proposed algorithm predicts attractive outcomes in terms of meanabsolute relative error plus mean relative error compared to the other existingmodels that justify the reliability prediction of the proposed model. Thus, thisnovel technique intends to make this model as simple as possible to improvethe software reliability.

Automatic License Plate Recognition System for Vehicles Using a CNN

Automatic License Plate Recognition(ALPR)systems are important in Intelligent Transportation Services(ITS)as they help ensure effective law enforcement and security.These systems play a significant role in border surveillance,ensuring safeguards,and handling vehicle-related crime.The most effective approach for implementing ALPR systems utilizes deep learning via a convolutional neural network(CNN).A CNN works on an input image by assigning significance to various features of the image and differentiating them from each other.CNNs are popular for license plate character recognition.However,little has been reported on the results of these systems with regard to unusual varieties of license plates or their success at night.We present an efficient ALPR system that uses a CNN for character recognition.A combination of pre-processing and morphological operations was applied to enhance input image quality,which aids system efficiency.The system has various features,such as the ability to recognize multi-line,skewed,and multifont license plates.It also works efficiently in night mode and can be used for different vehicle types.An overall accuracy of 98.13%was achieved using the proposed CNN technique.

Secure Rotation Invariant Face Detection System for Authentication

Biometric applications widely use the face as a component for recognition and automatic detection.Face rotation is a variable component and makes face detection a complex and challenging task with varied angles and rotation.This problem has been investigated,and a novice algorithm,namely RIFDS(Rotation Invariant Face Detection System),has been devised.The objective of the paper is to implement a robust method for face detection taken at various angle.Further to achieve better results than known algorithms for face detection.In RIFDS Polar Harmonic Transforms(PHT)technique is combined with Multi-Block Local Binary Pattern(MBLBP)in a hybrid manner.The MBLBP is used to extract texture patterns from the digital image,and the PHT is used to manage invariant rotation characteristics.In this manner,RIFDS can detect human faces at different rotations and with different facial expressions.The RIFDS performance is validated on different face databases like LFW,ORL,CMU,MIT-CBCL,JAFFF Face Databases,and Lena images.The results show that the RIFDS algorithm can detect faces at varying angles and at different image resolutions and with an accuracy of 99.9%.The RIFDS algorithm outperforms previous methods like Viola-Jones,Multi-blockLocal Binary Pattern(MBLBP),and Polar HarmonicTransforms(PHTs).The RIFDS approach has a further scope with a genetic algorithm to detect faces(approximation)even from shadows.