An Optimized Approach to Vehicle-Type Classification Using a Convolutional Neural Network

There are numerous application areas of computing similarity between process models.It includes finding similar models from a repository,controlling redundancy of process models,and finding corresponding activities between a pair of process models.The similarity between two process models is computed based on their similarity between labels,structures,and execution behaviors.Several attempts have been made to develop similarity techniques between activity labels,as well as their execution behavior.However,a notable problem with the process model similarity is that two process models can also be similar if there is a structural variation between them.However,neither a benchmark dataset exists for the structural similarity between process models nor there exist an effective technique to compute structural similarity.To that end,we have developed a large collection of process models in which structural changes are handcrafted while preserving the semantics of the models.Furthermore,we have used a machine learning-based approach to compute the similarity between a pair of process models having structural and label differences.Finally,we have evaluated the proposed approach using our generated collection of process models.

Sparse Crowd Flow Analysis of Tawaaf of Kaaba During the COVID-19 Pandemic

The advent of the COVID-19 pandemic has adversely affected the entire world and has put forth high demand for techniques that remotely manage crowd-related tasks.Video surveillance and crowd management using video analysis techniques have significantly impacted today’s research,and numerous applications have been developed in this domain.This research proposed an anomaly detection technique applied to Umrah videos in Kaaba during the COVID-19 pandemic through sparse crowd analysis.Managing theKaaba rituals is crucial since the crowd gathers from around the world and requires proper analysis during these days of the pandemic.The Umrah videos are analyzed,and a system is devised that can track and monitor the crowd flow in Kaaba.The crowd in these videos is sparse due to the pandemic,and we have developed a technique to track the maximum crowd flow and detect any object(person)moving in the direction unlikely of the major flow.We have detected abnormal movement by creating the histograms for the vertical and horizontal flows and applying thresholds to identify the non-majority flow.Our algorithm aims to analyze the crowd through video surveillance and timely detect any abnormal activity tomaintain a smooth crowd flowinKaaba during the pandemic.

An Improved iBAT-COOP Protocol for Cooperative Diversity in FANETs

Flying ad hoc networks(FANETs)present a challenging environment due to the dynamic and highly mobile nature of the network.Dynamic network topology and uncertain node mobility structure of FANETs do not aim to consider only one path transmission.Several different techniques are adopted to address the issues arising in FANETs,from game theory to clustering to channel estimation and other statistical schemes.These approaches mostly employ traditional concepts for problem solutions.One of the novel approaches that provide simpler solutions to more complex problems is to use biologically inspired schemes.Several Nature-inspired schemes address cooperation and alliance which can be used to ensure connectivity among network nodes.One such species that resembles the dynamicity of FANETs are Bats.In this paper,the biologically inspired metaheuristic technique of the BAT Algorithm is proposed to present a routing protocol called iBATCOOP(Improved BAT Algorithm using Cooperation technique).We opt for the design implementation of the natural posture of bats to handle the necessary flying requirements.Moreover,we envision the concept of cooperative diversity using multiple relays and present an iBAT-COOP routing protocol for FANETs.This paper employs cooperation for an optimal route selection and reflects on distance,Signal to Noise Ratio(SNR),and link conditions to an efficient level to deal with FANET’s routing.By way of simulations,the performance of iBAT-COOP protocol outperforms BAT-FANET protocol and reduces packet loss ratio,end-to-end delay,and transmission loss by 81%,21%,and 82%respectively.Furthermore,the average link duration is improved by 25%compared to the BAT-FANET protocol.

Learning Patterns from COVID-19 Instances

Coronavirus disease,which resulted from the SARS-CoV-2 virus,has spread worldwide since early 2020 and has been declared a pandemic by the World Health Organization(WHO).Coronavirus disease is also termed COVID-19.It affects the human respiratory system and thus can be traced and tracked from the Chest X-Ray images.Therefore,Chest X-Ray alone may play a vital role in identifying COVID-19 cases.In this paper,we propose a Machine Learning(ML)approach that utilizes the X-Ray images to classify the healthy and affected patients based on the patterns found in these images.The article also explores traditional,and Deep Learning(DL)approaches for COVID-19 patterns from Chest X-Ray images to predict,analyze,and further understand this virus.The experimental evaluation of the proposed approach achieves 97.5% detection performance using the DL model for COVID-19 versus normal cases.In contrast,for COVID-19 versus Pneumonia Virus scenario,we achieve 94.5% accurate detections.Our extensive evaluation in the experimental section guides and helps in the selection of an appropriate model for similar tasks.Thus,the approach can be used for medical usages and is particularly pertinent in detecting COVID-19 positive patients using X-Ray images alone.

X-ray Image-Based COVID-19 Patient Detection Using Machine Learning-Based Techniques

In early December 2019,the city of Wuhan,China,reported an outbreak of coronavirus disease(COVID-19),caused by a novel severe acute respiratory syndrome coronavirus-2(SARS-CoV-2).On January 30,2020,the World Health Organization(WHO)declared the outbreak a global pandemic crisis.In the face of the COVID-19 pandemic,the most important step has been the effective diagnosis and monitoring of infected patients.Identifying COVID-19 using Machine Learning(ML)technologies can help the health care unit through assistive diagnostic suggestions,which can reduce the health unit's burden to a certain extent.This paper investigates the possibilities of ML techniques in identifying/detecting COVID-19 patients including both conventional and exploring from chest X-ray images the effect of viral infection.This approach includes preprocessing,feature extraction,and classification.However,the features are extracted using the Histogram of Oriented(HOG)and Local Binary Pattern(LBP)feature descriptors.Furthermore,for the extracted features classification,six ML models of Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)is used.Experimental results show that the diagnostic accuracy of random forest classifier(RFC)on extracted HOG plusLBP features is as high as 94%followed by SVM at 93%.The sensitivity of the K-nearest neighbour model has reached an accuracy of 88%.Overall,the predicted approach has shown higher classification accuracy and effective diagnostic performance.It is a highly useful tool for clinical practitioners and radiologists to help them in diagnosing and tracking the cases of COVID-19.

Hajj Crowd Management Using CNN-Based Approach

Hajj as the Muslim holy pilgrimage,attracts millions of humans to Mecca every year.According to statists,the pilgrimage has attracted close to 2.5 million pilgrims in 2019,and at its peak,it has attracted over 3 million pilgrims in 2012.It is considered as the world’s largest human gathering.Safety makes one of the main concerns with regards to managing the large crowds and ensuring that stampedes and other similar overcrowding accidents are avoided.This paper presents a crowd management system using image classification and an alarm system for managing the millions of crowds during Hajj.The image classification system greatly relies on the appropriate dataset used to train the Convolutional neural network(CNN),which is the deep learning technique that has recently attracted the interest of the research community and industry in varying applications of image classification and speech recognition.The core building block of CNN is is a convolutional layer obtained by the getting CNN trained with patches bearing designated features of the trainee mages.The algorithm is implemented,using the Conv2D layers to activate the CNN as a sequential network.Thus,creating a 2D convolution layer having 64 filters and drop out of 0.5 makes the core of a CNN referred to as a set of KERNELS.The aim is to train the CNN model with mapped image data,and to make it available for use in classifying the crowd as heavily-crowded,crowded,semi-crowded,light crowded,and normal.The utility of these results lies in producing appropriate signals for proving helpful in monitoring the pilgrims.Counting pilgrims from the photos will help the authorities to determine the number of people in certain areas.The results demonstrate the utility of agent-based modeling for Hajj pilgrims.