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Flow and heat transfer of a nanofluid through a porous medium due to stretching/shrinking sheet with suction,magnetic field and thermal radiation

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applied mathematics(A Journal of Chinese Universities) 2023年第3期38卷 373-391页

作者： Ubaidullah Yashkun Khairy Zaimi Suliadi Firdaus Sufahani Mohamed REid Mohammad Ferdows Centre of Excellence for Social Innovation and Sustainability Institute of EngineeringUniversiti Malaysia PerlisPauh Putra Campus02600ArauPerlisMalaysia Department of Mathematics Sukkur IBA UniversityAirport RoadSukkur65200SindhPakistan Department of Mathematics and Statistics Faculty of Applied Sciences and TechnologyUniversiti Tun Hussein Onn MalaysiaJohorMalaysia Department of Mathematics Faculty of ScienceNew Valley UniversityAl-Wadi Al-GadidAl-Kharga 72511Egypt Mathematics Department Faculty of ScienceNorthern Border UniversityArar 1321Saudi Arabia Research Group of Fluid Flow Modeling and Simulation Department of Applied MathematicsUniversity of DhakaDhaka-1000Bangladesh

This study investigates the suction and magnetic field effects on the two-dimensional nanofluid flow through a stretching/shrinking sheet at the stagnation point in the porous medium with thermal *** governing partial differential equations(PDEs)are converted into ordinary differential equations(ODEs)using the similarity *** resulting ODEs are then solved numerically by using the bvp4c solver in MATLAB *** was found that dual solutions exist for the shrinking parameter values up to a certain *** numerical results obtained are compared,and the comparison showed a good agreement with the existing results in the *** governing parameters’effect on the velocity,temperature and nanoparticle fraction fields as well as the skin friction coefficient,the local Nusselt number and the Sherwood number are represented graphically and *** variation of the velocity,temperature and concentration increase with the increase in the suction and magnetic field *** seems that the thermal radiation effect has increased the local Sherwood number while the local Nusselt number is reduced with it.

关键词： stagnation point flow nanofluid porous medium suction magnetohydrodynamic thermal radiation

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An upgraded variant of backtracking search algorithm and its application to COVID-19 CT images segmentation

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Multimedia Tools and Applications 2025年 1-57页

作者： Debnath, Sanjib Nama, Sukanta Chakraborty, Sanjoy Saha, Apu Kumar Mirjalili, Seyedali Department of Computer Science & Engineering National Institute of Technology Agartala Tripura Agartala799046 India Department of Science & Humanities Gomati District Polytechnic Fulkumari Tripura Udaipur799013 India Department of Information Technology Bir Bikram Memorial College Tripura Agartala India Department of Mathematics National Institute of Technology Agartala Tripura Agartala799046 India Center for Artificial Intelligence Research and Optimization Torrens University Australia Brisbane Australia Faculty of Electrical Engineering and Computer Science VŠB-TU Ostrava Ostrava Czech Republic University Research and Innovation Center Obuda University Budapest Hungary Department of Computer Science & Engineering The ICFAI University Tripura Tripura 799210 India

To improve the performance of the original backtracking search algorithm (BSA), this work combines BSA with a centralized population initialization and an elitism-based local escape operator and proposes a new, improved variant of BSA called CiBSA. BSA's performance is improved by centralized population initialization regarding search capability, convergence speed, and capacity to jump out of the local optimum. The suggested algorithm's elitism-based local escape operator method prevents BSA searching slowness and enhances convergence rates and local search efficiency. The performance of the proposed algorithm has been tested on a set of 54 test suites. An extensive comparison with other recent meta-heuristics algorithms (MA) demonstrates that both methodologies substantially assist BSA in boosting the quality of its solutions and speeding up the convergence rate. The proposed CiBSA is then applied to a real-world problem: image segmentation using Kapur's entropy function. The proposed CiBSA is successfully applied to the segmentation of COVID-19 CT images using Kapur's entropy for grey-scale images. Considering the performance matrices Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Feature Similarity Index (FSIM), and Intersection over Union (IoU), the suggested CiBSA outperformed conventional MA algorithms. In COVID-19 CT image segmentation trials, CiBSA has superior segmentation capability and adaptability at varied threshold levels than other approaches. As a result, CiBSA has a lot of potential for enhancing COVID-19 diagnosis accuracy. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Signal to noise ratio

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A new parametric formulation of Baskakov-Schurer-Szász operators with approximation properties

Advanced Studies: Euro-Tbilisi Mathematical Journal

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Advanced Studies: Euro-Tbilisi Mathematical Journal 2025年第1期18卷 251-274页

作者： Braha, Naim L. Mansour, Toufik Özger, Faruk Mursaleen, M. Department of Mathematics and Computer Sciences University of Prishtina Avenue Mother Teresa No-5 Prishtine 10000 Serbia Department of Mathematics University of Haifa Haifa 3498838 Israel Department of Computer Engineering Igdır University Igdır 76000 Turkey Department of Mathematical Sciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai 602105 India Department of Medical Research China Medical University Hospital China Medical University (Taiwan) Taichung Taiwan Department of Mathematics Uşak University Uşak 64000 Turkey

In this study, we introduce a novel parametric extension of Baskakov-Schurer-Szász operators, which extends the established classical version of these operators. We establish a Korovkin-type theorem for this class, along with a Grüss-Voronovskaya-type result, and analyze the rate of convergence. Beyond the standard convergence analysis, we extend our investigation into weighted function spaces, where the behavior of the parametric operators is examined in the presence of varying function weights. This aspect is particularly important for approximating functions with non-uniform behavior or those defined over unbounded domains. Moreover, we focus on the shape-preserving properties of the parametric operators. We prove that, under certain conditions, these operators preserve key geometric characteristics of the functions they approximate, such as monotonicity and convexity. This is a crucial feature in applications where the structural integrity of the original function must be maintained. Furthermore, we show that the classical Baskakov-Schurer-Szász operators emerge as a special case of our parametric generalization, thereby encompassing a broader range of operator classes and providing a more unified theoretical framework for approximation operators. © 2025 Tbilisi Centre for Mathematical Sciences. All rights reserved.

关键词： parametric generalization rate of convergence shape-preserving

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Deep-Learning Super-Resolution for High-Speed Excitation-Emission Matrix Measurements 4

Deep-Learning Super-Resolution for High-Speed Excitation-Emi...

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AI and Optical Data Sciences IV 2023

作者： Michelucci, Umberto Fluri, Silvan Baumgartner, Michael Venturini, Francesca TOELT LLC Research and Development Dübendorf Switzerland Lucerne University of Applied Sciences and Arts Computer Science Department Lucerne Switzerland Institute of Applied Mathematics and Physics Zurich University of Applied Sciences Winterthur Switzerland

ISBN: (纸本)9781510659810

In many optical experiments, a long measurement time is necessary to collect enough information and improve the signal-to-noise ratio. This happens, for example, in total luminescence spectroscopy (TLS) where the data is acquired as excitation-emission matrices (EEMs). An EEM is an unique chemical fingerprint of the analyzed substance that allows its comprehensive characterization. To collect a high-resolution EEM, it is necessary to scan both the excitation and the emission wavelengths in small steps and, for each step, to collect the light for a long time to maximize the signal-to-noise ratio. Therefore, acquiring a high-resolution excitation emission matrix can take more than an hour, depending on the size of the wavelength steps, the intensity of the signal, and the spectral range to be analyzed. This paper proposes a new method to reconstruct a high-resolution EEM from low-resolution one using deep learning super-resolution techniques. Specifically, this work proposes a new artificial neural network architecture, a sub-pixel convolutional neural network, designed to be applied to fluorescence EEM images. The code used is made available via a GitHub repository with instructions for applying transfer learning to different types of images. © 2023 SPIE.

关键词： Signal to noise ratio

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Jyoti: An Intelligent App for Women Security 1

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4th International Conference on Inventive Computation and Information Technologies, ICICIT 2022

作者： Pathak, Pooja Choudhary, Parul Department of Mathematics Institute of Applied Sciences GLA University Uttar Pradesh Mathura India Department of Computer Application and Engineering GLA University Uttar Pradesh Mathura India

ISBN: (数字)9789811974021

ISBN: (纸本)9789811974014

In today’s world of rapid urbanization, a new research chapter in social relations and women’s safety has begun. With this wave of urban transformation comes a growing sense of loneliness and helplessness among certain social groups. Women’s safety is a major issue in this situation, not only for the poor but also for civilized women. Weaknesses in the situation women’s safety plans, practices, and guidelines exist to reduce violence against women and their fear of crime. While alone walking or traveling, women should be aware of their surroundings. The advancement of technology may also contribute to women’s safety. Our proposed application is "Jyoti" for women’s safety. This application helps to prevent female homicide. When a female is in a bad situation, the application will send a message to trusted people and the police will go to current location. This application has unique feature that it sends messages to trusted people at regular intervals while shaking the phone. It also calls for the first trusted contact to assist in an emergency. It will help to reduce female crime. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Global positioning system

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A Novel Approach for Performance Evaluation and Effectiveness of Data-Driven Heart Disease Diagnosis

A Novel Approach for Performance Evaluation and Effectivenes...

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International Conference on Advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2023

作者： Islam, Md Aminul Nag, Anindya Das, Ayontika Faruque, Jobaer Shuvo, Shabbir Ahmed Nur, Abdullah Hafez Rahman, Md Habibur School of Engineering Computing and Mathematics Oxford Brookes University Oxford United Kingdom Department of Computer Science and Engineering Adamas University Kolkata India IRD Dhaka Bangladesh Offenburg University of Applied Sciences Offenburg Germany International Islamic University Chittagong Sonaichhari Bangladesh Department of Mathematics University of Chittagong Chittagong Bangladesh

ISBN: (纸本)9789819795062

Cardiovascular disease (CVD) has emerged as a prominent cause of mortality globally during the past few decades. To receive medical care for heart illness in a clinic or medical center, it is necessary to undergo costly diagnostic tests. Implementing machine learning (ML) algorithms presents a viable approach to monitoring the health of individuals with cardiac conditions, offering a potential solution to mitigate the financial burden of medical expenses. This study employed ten ML algorithms to predict heart disease, which distinguishes itself from others by incorporating three distinct cross-validation approaches and three sample techniques, enhancing our findings’ accuracy. Three different cross-validation strategies have been implemented in this study: holdout cross-validation, tenfold cross-validation, and stratified tenfold cross-validation. Each technique has been applied to every sample technique used in the study. DTC, RFC, KNN, and XGB algorithms exhibited the most favorable outcomes. Our recommended model is the RF Classifier implemented with near-miss under-sampling out of the four models considered. The model under consideration demonstrates the maximum level of accuracy, specifically 97.54%, 97.11%, and 99%, while employing the Extra Tree, Random Forest Classifier, and XGB. The ML model's performance was assessed using metrics like accuracy, error rate, precision, recall, F1-score, ROC AUC curve etc. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cardiology

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Deep Contrastive Learning Network for Small-Sample Hyperspectral Image Classification

Journal of Remote Sensing

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Journal of Remote Sensing 2023年第1期3卷 74-90页

作者： Quanyong Liu Jiangtao Peng Genwei Zhang Weiwei Sun Qian Du Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and StatisticsHubei UniversityWuhanChina Department of Gas Sensors and Chemometrics State Key Laboratory of NBC Protection for CivilianBeijingChina Department of Geography and Spatial Information Techniques Ningbo UniversityNingboChina Department of Electrical and Computer Engineering Mississippi State UniversityMississippi StateUSA

Recently,deep learning methods have been widely used in hyperspectral image(HSI)classification and achieved good ***,the performance of these methods may be limited because of the scarcity of labeled samples in HSI *** solve the small-sample classification problem,a deep contrastive learning network(DCLN)method is proposed in this *** proposed DCLN method first constructs contrastive groups and trains the network through contrastive ***,it uses the trained network to extract spectral-spatial features of HSI pixels and generates pseudo-label for each unlabeled sample based on the spatial-spectral mixing ***,the pseudo-labeled samples with higher confidence are selected and added to the original training set to retrain the *** gradually increasing pseudo-labeled samples and refining the contrastive learning network,the model shows good feature learning ability and classification performance with the limited labeled *** results on 4 public HSI datasets demonstrate that the proposed DCLN method can achieve better performance than existing state-of-the-art methods.

关键词： DCL Deep network

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Developing an Artificial Intelligence Tool for Image Generation Using a Unique Dataset with Image-to-Image Functionality 24

Developing an Artificial Intelligence Tool for Image Generat...

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25th International Conference on computer Systems and Technologies, CompSysTech 2024

作者： Toktosunova, Akbiike Ergeshov, Alymbek Esenalieva, Gulzada Ermakov, Andrei Isaev, Ruslan Department of Applied Mathematics and Informatics Ala-Too International University Kyrgyzstan Department of Compute Science Ala-Too International University Kyrgyzstan Faculty of Engineering and Computer Science Ala-Too International University Kyrgyzstan

ISBN: (纸本)9798400716843

This article describes the application of a unique artificial intelligence model, trained on the basis of a special dataset, for drawing images. Artificial intelligence is very important in the modern world because it is a key driver of progress, offering innovative solutions to improve processes in various fields. A feature of this work is the use of the Stable Diffusion model and the creation of the LoRa model to create a specific object. The use of a special dataset, which is a classified set of images, to train models based on a stable propagation mechanism. The application of the image-to-image method, which consists in completely generating an image by having the model reproduce all the objects in the image, trying to preserve the maximum amount of detail in the image. As a result, a tool has been developed that can generate images of characters of a certain style. © 2024 ACM.

关键词： Adversarial machine learning

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Minimizing energy consumption in fixed node networks using a novel neutrosophic model: comparative analysis with standard and existing algorithms

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International Journal of Information Technology (Singapore) 2025年 1-15页

作者： Ghimire, Sangam Bhurtel, Nirjal Jha, Sudan Ahmad, Sultan Abdeljaber, Hikmat A. M. Nazeer, Jabeen Department of Computer Science and Engineering Kathmandu University 28 kilo Bagmati Dhulikhel 45210 Nepal Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-kharj 11942 Saudi Arabia School of Computer Science and Engineering Lovely Professional University Punjab Phagwara 144411 India Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Lebanon

The growing development and utilization of networked systems has led to more concern regarding the energy efficiency of these systems. In this paper, we present a novel approach to minimizing energy consumption in fixed node network for which we propose a new Neutrosophic model. This model leverages the unique strengths of Neutrosophic logic to enhance the efficiency by minimizing the energy consumption. The simulation of results is performed using Matplotlib (Python) with implementation parameters such as number of nodes (n) and additive white Gaussian noise (AWGN) (η). Additionally, we show a comparative analysis between the proposed model and other prevalent approaches used in minimizing energy consumption in networked systems. The comparative results shows improvement in energy efficiency, showcasing the potential of the proposed model as an alternative for optimizing energy consumption in networked systems. This work highlights the effectiveness of the proposed model and opens new avenues for future advancements in energy-efficient networked system using Neutrosophic approach. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Comparative analysis Energy-consumption optimization Networked systems Neutrosophic model Sensitivity analysis

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Enhancing Performances of Deep Neural Networks with Ensemble Learning Methods for Complex Human Activity Recognition Using Wearable Sensors 10

Enhancing Performances of Deep Neural Networks with Ensemble...

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10th International Conference on Digital Arts, Media and Technology and 8th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Mekruksavanich, Sakorn Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798331543273

Sensor-based human activity recognition (HAR) is a widely explored research area focused on identifying human behaviors by analyzing sensor-generated data, particularly one-dimensional time series signals. Such HAR systems have many real-world applications, including elderly health monitoring and athlete performance evaluation. This paper presents Ens-DeepNet, a novel ensemble learning approach designed for recognizing complex human activities using data from wearable sensors. While deep neural networks have proven effective for HAR tasks, their performance can be further improved through ensemble strategies. To this end, we propose an ensemble architecture that combines five distinct deep learning models - convolutional neural network (CNN), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and bidirectional GRU (BiGRU). These models are integrated using a majority voting scheme to boost classification accuracy. The proposed method is evaluated using the UTwente dataset, which includes sensor data collected from ten individuals via tri-axial accelerometers and gyroscopes. Experimental findings demonstrate that the ensemble model achieves an impressive accuracy of 99.28%, surpassing the performance of each standalone model, whose accuracies range from 92.77%to 97.34%. This performance gain is particularly notable in recognizing complex activities such as eating, drinking coffee, smoking, and conversing. © 2025 IEEE.

关键词： Deep neural networks

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