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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Khan, Shujahat Ali Bajwa, Hasan Raza Sundaram, Jawahar Pritika Shanmugam, Bharanidharan Energy and Resources Institute Faculty of Science and Technology Charles Darwin University Darwin Australia Christ Academy Institute for Advanced Studies Department of Computer Science and Applications Bengluru India

ISBN: (纸本)9798350371314

Wearable devices are becoming more popular these days due to its unique features and its effectiveness. The evolution of Internet of Things (IoT) that combined health care devices and sensors has entirely changed the scenario in the healthcare infrastructure. This innovation will confidently improve the existing healthcare facilities and practices due to the continuous observing of patient's health even in the remote places. Regardless of their effectiveness, IoT devices are insecure and are vulnerable to security threats that should need to be overcome. The main issue is to protect from outside entities or attacks that may obstruct the advancement and put the individual's information at risk. This study focuses on outlining the vulnerabilities focusing on smartwatches in the healthcare IoT domain. Furthermore, it has been observed that a limited Bluetooth Low Energy (BLE) based smartwatches has a very weak or no security defense system making these devices vulnerable to various security threats such as Brute-force attacks, Man-in-The-Middle (MiTM) attacks, Denial of Service (DoS) attacks, and Phishing. Experimental testing has been performed to test the vulnerabilities in smartwatch devices to verify the data integrity concerns. By using Bluetooth sniffing tools, the security of the smart wearable devices has been tested to identify its weaknesses and to target the BLE devices. Attempts have been made to tamper its parameters by gaining access to the devices through Bluetooth connection. The overall result reveals that these devices are vulnerable to different exploits. This study provides the foundation to assess possible vulnerabilities in smart wearable devices. Furthermore, it highlights the loopholes existed in these devices which can be exploited. © 2024 IEEE.

关键词： Bluetooth

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Prediction of cyanotic and acyanotic congenital heart disease using machine learning models

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World Journal of Clinical Pediatrics 2024年第4期13卷 15-24页

作者： Sana Shahid Haris Khurram Apiradee Lim Muhammad Farhan Shabbir Baki Billah Department of Statistics Bahauddin Zakariya UniversityMultan 60000PunjabPakistan Department of Mathematics and Computer Science Faculty of Science and TechnologyPrince of Songkla UniversityPattani CampusPattani 94000Thailand Department of Science and Humanities National University of Computer and Emerging SciencesChiniot-Faisalabad CampusChiniot 35400PunjabPakistan Department of Cardiology Chaudhary Pervaiz Elhai Institute of CardiologyMultan 60000PunjabPakistan School of Public Health and Preventive Medicine Monash UniversityMelbourne 3000VictoriaAustralia

BACKGROUND Congenital heart disease is most commonly seen in neonates and it is a major cause of pediatric illness and childhood morbidity and *** To identify and build the best predictive model for predicting cyanotic and acyanotic congenital heart disease in children during pregnancy and identify their potential risk *** The data were collected from the Pediatric Cardiology department at Chaudhry Pervaiz Elahi Institute of Cardiology Multan,Pakistan from December 2017 to October 2019.A sample of 3900 mothers whose children were diagnosed with identify the potential *** machine learning models were compared,and the best-fitted model was selected using the area under the curve,sensitivity,and specificity of the *** Out of 3900 patients included,about 69.5%had acyanotic and 30.5%had cyanotic congenital heart *** had more cases of acyanotic(53.6%)and cyanotic(54.5%)congenital heart disease as compared to *** odds of having cyanotic was 1.28 times higher for children whose mothers used more fast food frequently during *** artificial neural network model was selected as the best predictive model with an area under the curve of 0.9012,sensitivity of 65.76%,and specificity of 97.23%.CONCLUSION Children having a positive family history are at very high risk of having cyanotic and acyanotic congenital heart *** are more at risk and their mothers need more care,good food,and physical activity during *** best-fitted model for predicting cyanotic and acyanotic congenital heart disease is the artificial neural *** results obtained and the best model identified will be useful for medical practitioners and public health scientists for an informed decision-making process about the earlier diagnosis and improve the health condition of children in Pakistan.

关键词： Congenital heart disease Cyanotic heart disease Acyanotic heart disease Logistic regression model Artificial neural network

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A high-order predictor–corrector method for initial value problems with fractional derivative involving Mittag–Leffler kernel

A high-order predictor–corrector method for initial value p...

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作者： Aljhani, Sami Noorani, Mohd Salmi Md Douaifia, Redouane Abdelmalek, Salem Department of Mathematical Sciences Faculty of Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi Malaysia Process Engineering Department Faculty of Technology University of Saad Dahlab Blida 1 Blida Algeria Echahid Cheikh Larbi Tebessi University Tebessa Algeria Department of Mathematics and Computer Science Echahid Cheikh Larbi Tebessi University Tebessa Algeria

In this paper, we propose a numerical scheme of the predictor–corrector type for solving nonlinear fractional initial value problems;the chosen fractional derivative is called the Atangana–Baleanu derivative defined in Caputo sense (ABC). This proposed method is based on Lagrangian quadratic polynomials to approximate the nonlinearity implied in the Volterra integral, which is obtained by reducing the given fractional differential equation via the properties of the ABC-fractional derivative. Through this technique, we get corrector formula with high accuracy, which is implicit, as well as predictor formula, which is explicit and has the same precision order as the corrective formula. On the other hand, the so-called memory term is computed only once for both prediction and correction phases, which indicates the low cost of the proposed method. Also, the error bound of the proposed numerical scheme is offered. Furthermore, numerical experiments are presented in order to assess the accuracy of the new method on two differential equations. Moreover, a case study is considered where the proposed predictor–corrector scheme is used to obtain approximate solutions of a coupled nonautonomous ABC-fractional differential system. © 2023 John Wiley & Sons, Ltd.

关键词： Initial value problems

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Addressing Local and Regional Recharging Demand: Allocation of Charging Stations through Iterative Route Analysis 15

Addressing Local and Regional Recharging Demand: Allocation ...

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15th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2024 / The 7th International Conference on Emerging Data and Industry 4.0, EDI40 2024

作者： Fredriksson, Henrik Dahl, Mattias Holmgren, Johan Lövström, Benny Department of Mathematics and Natural Sciences Blekinge Institute of Technology Karlskrona37179 Sweden Department of Computer Science and Media Technology Malmö University Malmö20506 Sweden

The emergence of electric vehicles offers a promising approach to achieving a more sustainable transportation system, given their lower production of direct emissions. However, the limited driving range and insufficient public recharging infrastructure in some areas hinder their competitiveness against traditional vehicles with internal combustion engines. To address these issues, this paper introduces an "iterative route cover optimization method" to suggest charging station locations in high-demand regions. The method samples routes from a route choice set and optimally locates at least one charging station along each route. Through iterative resampling and optimal allocation of charging stations, the method identifies the potential recharging demand in a location or a region. We demonstrate the method's applicability to a transportation network of the southern part of Sweden. The results show that the proposed method is capable to suggest locations and geographical regions where the recharging demand is potentially high. © 2024 Elsevier B.V.. All rights reserved.

关键词： Location

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Effective Classification of Synovial Sarcoma Cancer Using Structure Features and Support Vectors

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computers, Materials & Continua 2022年第8期72卷 2521-2543页

作者： P.Arunachalam N.Janakiraman Junaid Rashid Jungeun Kim Sovan Samanta Usman Naseem Arun Kumar Sivaraman A.Balasundaram Department of Electronics and Communication Engineering K.L.N.College of EngineeringPottapalayam630612Tamil NaduIndia Department of Computer Science and Engineering Kongju National UniversityCheonan 31080Korea Department of Mathematics Tamralipta MahavidyalayaTamluk721636West Bengal School of Computer Science University of SydneyNSW 2006Australia School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India School of Computer Science and Engineering Centre for Cyber Physical SystemsVellore Institute of TechnologyChennai600127India

In this research work,we proposed a medical image analysis framework with two separate releases whether or not Synovial Sarcoma(SS)is the cell structure for *** this framework the histopathology images are decomposed into a third-level sub-band using a two-dimensional Discrete Wavelet ***,the structure features(SFs)such as PrincipalComponentsAnalysis(PCA),Independent ComponentsAnalysis(ICA)and Linear Discriminant Analysis(LDA)were extracted from this subband image representation with the distribution of wavelet *** SFs are used as inputs of the Support Vector Machine(SVM)***,classification of PCA+SVM,ICA+SVM,and LDA+SVM with Radial Basis Function(RBF)kernel the efficiency of the process is differentiated and compared with the best classification ***,data collected on the internet from various histopathological centres via the Internet of Things(IoT)are stored and shared on blockchain technology across a wide range of image distribution across secure data IoT *** to this,the minimum and maximum values of the kernel parameter are adjusted and updated periodically for the purpose of industrial application in device ***,these resolutions are presented with an excellent example of a technique for training and testing the cancer cell structure prognosis methods in spindle shaped cell(SSC)histopathological imaging *** performance characteristics of cross-validation are evaluated with the help of the receiver operating characteristics(ROC)curve,and significant differences in classification performance between the techniques are *** combination of LDA+SVM technique has been proven to be essential for intelligent SS cancer detection in the future,and it offers excellent classification accuracy,sensitivity,specificity.

关键词： Principal components analysis independent components analysis linear discriminant analysis support vector machine blockchain technology IoT application industry application

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Numerical Predictions of Laminar Forced Convection Heat Transfer with and without Buoyancy Effects from an Isothermal Horizontal Flat Plate to Supercritical Nitrogen

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Frontiers in Heat and Mass Transfer 2024年第3期22卷 889-917页

作者： K.S.Rajendra Prasad Sathya Sai T.R.Seetharam Adithya Garimella Department of Mechanical and Industrial Engineering Manipal Institute of Technology BengaluruManipal Academy of Higher EducationManipal576104India Sustainable Energy Technology Faculty of Electrical EngineeringMathematics and Computer ScienceDelft University of TechnologyDelft2600 AANetherlands Department of Mechanical Engineering PES UniversityBengaluru560085India

Numerical predictions are made for Laminar Forced convection heat transfer with and without buoyancy effects for Supercritical Nitrogen flowing over an isothermal horizontal flat plate with a heated surface facing *** are performed by varying the value ofΔT from5 to 30 K and P_(∞)/P_(cr)ratio from1.1 to *** of all the thermophysical properties of supercritical Nitrogen is *** wall temperatures are chosen in such a way that two values of Tw are less than T∗(T*is the temperature at which the fluid has a maximum value of Cp for the given pressure),one value equal to T∗and two values greater than T∗.Three different values of U∞are used to obtain Re∞range of 3.6×10_(4)to 4.74×10^(5)for forced convection without buoyancy effects and Gr_(∞)/Re^(2)_(∞)range of 0.011 to 3.107 for the case where buoyancy effects are *** different forms of correlations are proposed based on numerical predictions and are compared with actual numerical *** has been found that in all six forms of correlations,the maximum deviations are found to occur in those cases where the pseudocritical temperature TT∗lies between the wall temperature and bulk fluid temperature.

关键词： Supercritical nitrogen laminar flow numerical methods forced convection heat transfer isothermal horizontal surface

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Crops Leaf Diseases Recognition:A Framework of Optimum Deep Learning Features

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computers, Materials & Continua 2023年第1期74卷 1139-1159页

作者： Shafaq Abbas Muhammad Attique Khan Majed Alhaisoni Usman Tariq Ammar Armghan Fayadh Alenezi Arnab Majumdar Orawit Thinnukool Department of Computer Science HITEC UniversityTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia College of Computer Engineering and Science Prince Sattam Bin Abdulaziz UniversityAl-Kharaj11942Saudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySakakahSaudi Arabia Faculty of Engineering Imperial College LondonLondonSW72AZUK College of Arts Mediaand TechnologyChiang Mai UniversityChiang Mai50200Thailand

Manual diagnosis of crops diseases is not an easy process;thus,a computerized method is widely *** couple of years,advancements in the domain ofmachine learning,such as deep learning,have shown substantial ***,they still faced some challenges such as similarity in disease symptoms and irrelevant features *** this article,we proposed a new deep learning architecture with optimization algorithm for cucumber and potato leaf diseases *** proposed architecture consists of five *** the first step,data augmentation is performed to increase the numbers of training *** the second step,pre-trained DarkNet19 deep model is opted and fine-tuned that later utilized for the training of fine-tuned model through transfer *** features are extracted from the global pooling layer in the next step that is refined using Improved Cuckoo search *** best selected features are finally classified using machine learning classifiers such as SVM,and named a few more for final classification *** proposed architecture is tested using publicly available datasets–Cucumber National Dataset and Plant *** proposed architecture achieved an accuracy of 100.0%,92.9%,and 99.2%,*** with recent techniques is also performed,revealing that the proposed method achieved improved accuracy while consuming less computational time.

关键词： Crops diseases preprocessing convolutional neural network features optimization machine learning

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Enhancing face recognition performance: a comprehensive evaluation of deep learning models and a novel ensemble approach with hyperparameter tuning

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Soft Computing 2024年第20期28卷 12399-12424页

作者： Selvaganesan, Jana Sudharani, B. Shekhar, S. N. Chandra Vaishnavi, K. Priyadarsini, K. Raju, K. Srujan Rao, T. Srinivasa Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R & ampD Institute of Science and Technology Avadi Tamil Nadu Chennai600062 India Guntur Dist. Andhra Pradesh Vadlamudi India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and Technology Telangana Hyderabad501301 India Department of Computer Science and Engineering Sona College of Technology Tamil Nadu Salem636005 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Computer Science and Engineering CMR Technical Campus Medchal Telangana Hyderabad501401 India Department of Electronics and Communication Engineering Aditya Engineering College Surampalem533437 India

In response to growing security concerns and the increasing demand for face recognition (FR) technology in various sectors, this research explores the application of deep learning techniques, specifically pre-trained Convolutional Neural Network (CNN) models, in the field of FR. The study harnesses the power of five pre-trained CNN models—DenseNet201, ResNet152V2, MobileNetV2, SeResNeXt, and Xception—for robust feature extraction, followed by SoftMax classification. A novel weighted average ensemble model, meticulously optimized through a grid search technique, is introduced to augment feature extraction and classification efficacy. Emphasizing the significance of robust data pre-processing, encompassing resizing, data augmentation, splitting, and normalization, the research endeavors to fortify the reliability of FR systems. Methodologically, the study systematically investigates hyperparameters across deep learning models, fine-tuning network depth, learning rate, activation functions, and optimization methods. Comprehensive evaluations unfold across diverse datasets to discern the effectiveness of the proposed models. Key contributions of this work encompass the utilization of pre-trained CNN models for feature extraction, extensive evaluation across multiple datasets, the introduction of a weighted average ensemble model, emphasis on robust data pre-processing, systematic hyperparameter tuning, and the utilization of comprehensive evaluation metrics. The results, meticulously analyzed, unveil the superior performance of the proposed method, consistently outshining alternative models across pivotal metrics, including Recall, Precision, F1 Score, Matthews Correlation Coefficient (MCC), and Accuracy. Notably, the proposed method attains an exceptional accuracy of 99.48% on the labeled faces in the wild (LFW) dataset, surpassing erstwhile state-of-the-art benchmarks. This research represents a significant stride in FR technology, furnishing a dependable and accurate

关键词： Convolutional neural networks

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Classification COVID-19 Based on Enhancement X-Ray Images and Low Complexity Model

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computers, Materials & Continua 2022年第7期72卷 561-576页

作者： Aymen Saad Israa SKamil Ahmed Alsayat Ahmed Elaraby Department of Information Technology Technical College of ManagementKufaAl Furat Alawsat UniversityIraq Department of Medical Laboratory Techniques Al-Mustaqbal University CollegeHillaIraq Department of Computer Science College of Computer and Information SciencesJouf UniversitySaudi Arabia Department of Computer Science Faculty of Computers and InformationSouth Valley UniversityQenaEgypt

COVID-19 has been considered one of the recent epidemics that occurred at the last of 2019 and the beginning of 2020 that world *** spread of COVID-19 requires a fast technique for diagnosis to make the appropriate decision for the treatment.X-ray images are one of the most classifiable images that are used widely in diagnosing patients’data depending on radiographs due to their structures and tissues that could be *** Neural Networks(CNN)is the most accurate classification technique used to diagnose COVID-19 because of the ability to use a different number of convolutional layers and its high classification *** using CNNs techniques requires a large number of images to learn and obtain satisfactory *** this paper,we used SqueezNet with a modified output layer to classify X-ray images into three groups:COVID-19,normal,and *** this study,we propose a deep learning method with enhance the features of X-ray images collected from Kaggle,Figshare to distinguish between COVID-19,Normal,and Pneumonia *** this regard,several techniques were used on the selected image samples which are Unsharp filter,Histogram equal,and Complement image to produce another view of the *** Squeeze Net CNN model has been tested in two scenarios using the 13,437 X-ray images that include 4479 for each type(COVID-19,Normal and Pneumonia).In the first scenario,the model has been tested without any enhancement on the *** achieved an accuracy of 91%.But,in the second scenario,the model was tested using the same previous images after being improved by several techniques and the performance was high at approximately 95%.The conclusion of this study is the used model gives higher accuracy results for enhanced images compared with the accuracy results for the original images.A comparison of the outcomes demonstrated the effectiveness of ourDLmethod for classifying COVID-19 based on enhanced X-ray images.

关键词： COVID-19 X-ray image enhancement classification CNN SqueezNet model

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Modeling, Experimental Simulations And Probability Estimation Upon A Computationally-Efficient Discretization Algorithm For Jump-Type Lévy Processes 8

Modeling, Experimental Simulations And Probability Estimatio...

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8th International Symposium on Electrical and Electronics Engineering, ISEEE 2023

作者： Sbirna, Sebastian Sbirna, Liana-Simona Technical University of Denmark Institute of Applied Mathematics and Computer Science Dtu Compute Department Kongens Lyngby Denmark University of Craiova Faculty of Sciences Department of Chemistry Craiova Romania

ISBN: (纸本)9798350301670

The present work aims at modeling, experimentally simulating and then implementing a computationally-efficient algorithm for jump-type Lévy processes. Within the paper, a pseudocode version for discretizing the continuous Brownian motion component of the Lévy processes is presented. The study also analyzes, through simulation experiments, the effect of each of the three process components upon the overall motion variance, slope and volatility of the result. Further model experiments are performed by tuning the jump size of the internal Poisson component, using four different types of distributions - namely: exponential, hyperexponential, normal and Pareto, in order to study the behavior of the Lévy processes. Finally, first-passage probabilities are estimated upon all these four distributions, which have many applications in financial and economics fields, as well as in the fields of mathematics, physics and engineering. © 2023 IEEE.

关键词： Brownian movement

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