基于Cattaneo-Christov热通量模型的倾斜磁驱动Casson纳米流体在径向拉伸板上的流动

本文以二维倾斜磁驱动Casson纳米流体为对象,研究了其在驻点附近流过径向拉伸板时的传导、热辐射和放热/吸热现象。为了研究传热特性,采用了Cattaneo-Christov热通量模型。利用Buongiorno模型描述纳米流体流动问题方程,以探讨布朗运动、热泳、热滑移和质量滑移条件的影响。采用一组相似变换将研究模型的高阶非线性偏微分方程转化为常微分方程组。利用射击法的效率优势,对所提出的流动问题方程进行了数值管理。通过图表讨论了流动参数对流体速度、浓度和温度的影响。随着Casson参数的增大,流体速度加快,表面摩擦因数也增大;随着磁参数增大,流体的速度减慢。

Convolutional Neural Network Based Intelligent Handwritten Document Recognition

This paper presents a handwritten document recognition system based on the convolutional neural network technique.In today’s world,handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired users.This technology is also helpful for the automatic data entry system.In the proposed systemprepared a dataset of English language handwritten character images.The proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten documents.In this research,multiple experiments get very worthy recognition results.The proposed systemwill first performimage pre-processing stages to prepare data for training using a convolutional neural network.After this processing,the input document is segmented using line,word and character segmentation.The proposed system get the accuracy during the character segmentation up to 86%.Then these segmented characters are sent to a convolutional neural network for their recognition.The recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given dataset.The proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%,and for validation that accuracy slightly decreases with 90.42%.

Cloud-Based Diabetes Decision Support System Using Machine Learning Fusion

Diabetes mellitus,generally known as diabetes,is one of the most common diseases worldwide.It is a metabolic disease characterized by insulin deciency,or glucose(blood sugar)levels that exceed 200 mg/dL(11.1 ml/L)for prolonged periods,and may lead to death if left uncontrolled by medication or insulin injections.Diabetes is categorized into two main types—type 1 and type 2—both of which feature glucose levels above“normal,”dened as 140 mg/dL.Diabetes is triggered by malfunction of the pancreas,which releases insulin,a natural hormone responsible for controlling glucose levels in blood cells.Diagnosis and comprehensive analysis of this potentially fatal disease necessitate application of techniques with minimal rates of error.The primary purpose of this research study is to assess the potential role of machine learning in predicting a person’s risk of developing diabetes.Historically,research has supported the use of various machine algorithms,such as naïve Bayes,decision trees,and articial neural networks,for early diagnosis of diabetes.However,to achieve maximum accuracy and minimal error in diagnostic predictions,there remains an immense need for further research and innovation to improve the machine-learning tools and techniques available to healthcare professionals.Therefore,in this paper,we propose a novel cloud-based machine-learning fusion technique involving synthesis of three machine algorithms and use of fuzzy systems for collective generation of highly accurate nal decisions regarding early diagnosis of diabetes.

Modelling Intelligent Driving Behaviour Using Machine Learning

In vehicular systems,driving is considered to be the most complex task,involving many aspects of external sensory skills as well as cognitive intelligence.External skills include the estimation of distance and speed,time perception,visual and auditory perception,attention,the capability to drive safely and action-reaction time.Cognitive intelligence works as an internal mechanism that manages and holds the overall driver’s intelligent system.These cognitive capacities constitute the frontiers for generating adaptive behaviour for dynamic environments.The parameters for understanding intelligent behaviour are knowledge,reasoning,decision making,habit and cognitive skill.Modelling intelligent behaviour reveals that many of these parameters operate simultaneously to enable drivers to react to current situations.Environmental changes prompt the parameter values to change,a process which continues unless and until all processes are completed.This paper model intelligent behaviour by using a‘driver behaviour model’to obtain accurate intelligent driving behaviour patterns.This model works on layering patterns in which hierarchy and coherence are maintained to transfer the data with accuracy from one module to another.These patterns constitute the outcome of different modules that collaborate to generate appropriate values.In this case,accurate patterns were acquired using ANN static and dynamic non-linear autoregressive approach was used and for further accuracy validation,time-series dynamic backpropagation artificial neural network,multilayer perceptron and random sub-space on real-world data were also applied.

IoMT-Based Smart Monitoring Hierarchical Fuzzy Inference System for Diagnosis of COVID-19

The prediction of human diseases,particularly COVID-19,is an extremely challenging task not only for medical experts but also for the technologists supporting them in diagnosis and treatment.To deal with the prediction and diagnosis of COVID-19,we propose an Internet of Medical Things-based Smart Monitoring Hierarchical Mamdani Fuzzy Inference System(IoMTSM-HMFIS).The proposed system determines the various factors like fever,cough,complete blood count,respiratory rate,Ct-chest,Erythrocyte sedimentation rate and C-reactive protein,family history,and antibody detection(lgG)that are directly involved in COVID-19.The expert system has two input variables in layer 1,and seven input variables in layer 2.In layer 1,the initial identification for COVID-19 is considered,whereas in layer 2,the different factors involved are studied.Finally,advanced lab tests are conducted to identify the actual current status of the disease.The major focus of this study is to build an IoMT-based smart monitoring system that can be used by anyone exposed to COVID-19;the system would evaluate the user’s health condition and inform them if they need consultation with a specialist for quarantining.MATLAB-2019a tool is used to conduct the simulation.The COVID-19 IoMTSM-HMFIS system has an overall accuracy of approximately 83%.Finally,to achieve improved performance,the analysis results of the system were shared with experts of the Lahore General Hospital,Lahore,Pakistan.

Data and Machine Learning Fusion Architecture for Cardiovascular Disease Prediction

Heart disease,which is also known as cardiovascular disease,includes various conditions that affect the heart and has been considered a major cause of death over the past decades.Accurate and timely detection of heart disease is the single key factor for appropriate investigation,treatment,and prescription of medication.Emerging technologies such as fog,cloud,and mobile computing provide substantial support for the diagnosis and prediction of fatal diseases such as diabetes,cancer,and cardiovascular disease.Cloud computing provides a cost-efficient infrastructure for data processing,storage,and retrieval,with much of the extant research recommending machine learning(ML)algorithms for generating models for sample data.ML is considered best suited to explore hidden patterns,which is ultimately helpful for analysis and prediction.Accordingly,this study combines cloud computing with ML,collecting datasets from different geographical areas and applying fusion techniques to maintain data accuracy and consistency for the ML algorithms.Our recommended model considered three ML techniques:Artificial Neural Network,Decision Tree,and Naïve Bayes.Real-time patient data were extracted using the fuzzy-based model stored in the cloud.

Autonomous Parking-Lots Detection with Multi-Sensor Data Fusion Using Machine Deep Learning Techniques

The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of humanity.Vision-based target detection and object classification have been improved due to the development of deep learning algorithms.Data fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise,well-engineered,and complete detection of objects,scene or events.The target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion detection.In this study we examined to solve these problems described by(1)extracting region-of-interest in the images(2)vehicle detection based on instance segmentation,and(3)building deep learning model based on the key features obtained from input parking images.We build a deep machine learning algorithm that enables collecting real video-camera feeds from vision sensors and predicting free parking spaces.Image augmentation techniques were performed using edge detection,cropping,refined by rotating,thresholding,resizing,or color augment to predict the region of bounding boxes.A deep convolutional neural network F-MTCNN model is proposed that simultaneously capable for compiling,training,validating and testing on parking video frames through video-camera.The results of proposed model employing on publicly available PK-Lot parking dataset and the optimized model achieved a relatively higher accuracy 97.6%than previous reported methodologies.Moreover,this article presents mathematical and simulation results using state-of-the-art deep learning technologies for smart parking space detection.The results are verified using Python,TensorFlow,OpenCV computer simulation frameworks.

Joint Channel and Multi-User Detection Empowered with Machine Learning

The numbers of multimedia applications and their users increase with each passing day.Different multi-carrier systems have been developed along with varying techniques of space-time coding to address the demand of the future generation of network systems.In this article,a fuzzy logic empowered adaptive backpropagation neural network(FLeABPNN)algorithm is proposed for joint channel and multi-user detection(CMD).FLeABPNN has two stages.The first stage estimates the channel parameters,and the second performsmulti-user detection.The proposed approach capitalizes on a neuro-fuzzy hybrid systemthat combines the competencies of both fuzzy logic and neural networks.This study analyzes the results of using FLeABPNN based on a multiple-input andmultiple-output(MIMO)receiver with conventional partial oppositemutant particle swarmoptimization(POMPSO),total-OMPSO(TOMPSO),fuzzy logic empowered POMPSO(FL-POMPSO),and FL-TOMPSO-based MIMO receivers.The FLeABPNN-based receiver renders better results than other techniques in terms of minimum mean square error,minimum mean channel error,and bit error rate.