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Polyp image segmentation based on improved planet optimization algorithm using reptile search algorithm

Neural Computing and Applications 2025年第8期37卷 6327-6349页

作者： Abd Elaziz, Mohamed Al-qaness, Mohammed A. A. Al-Betar, Mohammed Azmi Ewees, Ahmed A. Department of Mathematics Faculty of Science Zagazig University Zagazig Egypt College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China Faculty of Computer Science and Engineering Galala University Suze435611 Egypt Ajman University Ajman346 United Arab Emirates College of Computing and Information Technology University of Bisha Bisha Saudi Arabia Department of Computer Damietta University Damietta Egypt Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan

To recognize the potential for colon polyps to develop into cancer over time, early diagnosis is crucial for preventative healthcare. Timely identification significantly improves the prognosis and treatment outcomes for colorectal cancer patients. Image segmentation is crucial in medical image analysis for accurate diagnosis and treatment planning. Therefore, in this study, we present an alternative multilevel thresholding polyp segmentation method (MPOA) to enhance the segmentation of polyp images. The proposed method is based on enhancing the planet optimization algorithm (POA) by integrating operators from the reptile search algorithm (RSA). The evaluation of the developed MPOA is tested with different polyp images and compared with other image segmentation approaches. The results highlight the superior capability of MPOA, as evidenced by various performance measures in effectively segmenting polyp images. Furthermore, metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and fitness values demonstrate that MPOA outperforms the basic version of POA and other methods. The evaluation outcomes underscore the significant impact of RSA in enhancing the performance of POA for the segmentation of polyp images. © The Author(s) 2025.

关键词： Diseases

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The Impact of Predictive Maintenance on the Performance of Industrial Enterprises

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SN computer Science 2025年第1期6卷 1-34页

作者： Er-Ratby, Mohamed Kobi, Abdessamad Sadraoui, Youssef Kadiri, Moulay Saddik LaSTI Laboratory of Science and Engineering Techniques National School of Applied Sciences of Khouribga Sultan Moulay Slimane University Khouribga Morocco LARIS Angevin Laboratory for Systems Engineering Research Polytech Angers University of Angers Angers France LIPIM Laboratory of Process Engineering Computer Science and Mathematics National School of Applied Sciences of Khouribga Sultan Moulay Slimane University Khouribga Morocco

Optimizing maintenance and enhancing the performance of businesses are significant concerns in the modern industrial world. Predictive maintenance is emerging as an innovative approach to address these challenges, allowing companies to shift from corrective maintenance to preventive maintenance. Predictive maintenance relies on the use of advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) to collect and analyse real-time data from equipment. Through this predictive analysis, it becomes possible to identify early warning signals of failures, enabling the anticipation of potential issues and the proactive planning of maintenance interventions. In this study, we will thoroughly examine the impact of predictive maintenance on the performance of businesses. We will explore the benefits and opportunities it offers in terms of reducing downtime, optimizing maintenance costs, and enhancing productivity. We will also investigate the various technologies and methods used in the implementation of predictive maintenance, along with potential challenges and best practices for successful adoption. This research focuses on studying the application of predictive maintenance within a company using data science and machine learning methods. Predictive maintenance represents an innovative approach aimed at anticipating equipment failures by leveraging real-time collected data. Through the analysis of this data using sophisticated algorithms, it becomes possible to identify early signals of potential problems and implement preventive maintenance actions before breakdowns occur. This approach not only reduces unexpected downtime but also optimizes maintenance operations by avoiding unnecessary interventions and maximizing resource utilization. The ultimate goal is to improve equipment availability, optimize operational performance, and maximize the overall yield of the company. © The Author(s), under exclusive licence to S

关键词： Artificial intelligence (AI) and machine learning (ML) Data science Optimizing maintenance Performance indicators Predictive maintenance Production

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Chaos-Based Novel Watermarked Satellite Image Encryption Scheme

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computer Modeling in engineering & Sciences 2025年第4期143卷 1049-1070页

作者： Mohamed Medani Yahia Said Nashwan Adnan Othman Farrukh Yuldashev Mohamed Kchaou Faisal Khaled Aldawood Bacha Rehman Applied College of Muhayil Aseer King Khalid UniversityAbha62529Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border UniversityArar73213Saudi Arabia Department of Computer Engineering College of EngineeringKnowledge UniversityErbil44001Iraq Department of Computer Engineering Al-Kitab UniversityAltun Kupri36001Iraq Department of Informatics and Its Teaching Methods Tashkent State Pedagogical UniversityTashkent100070Uzbekistan Department of Industrial Engineering College of EngineeringUniversity of BishaBishaP.O.Box 001Saudi Arabia Department of Computer Science Solent UniversitySouthamptonSO140YNUK

Satellite images are widely used for remote sensing and defence applications,however,they are subject to a variety of *** ensure the security and privacy of these images,theymust be watermarked and encrypted before ***,this paper proposes a novel watermarked satellite image encryption scheme based on chaos,Deoxyribonucleic Acid(DNA)sequence,and hash *** watermark image,DNA sequence,and plaintext image are passed through the Secure Hash Algorithm(SHA-512)to compute the initial condition(keys)for the Tangent-Delay Ellipse Reflecting Cavity Map(TD-ERCS),Henon,and Duffing chaotic maps,*** bitwise XOR and substitution,the TD-ERCS map encrypts the watermark *** ciphered watermark image is embedded in the plaintext *** embedded plaintext image is permuted row-wise and column-wise using the Henon chaotic *** permuted image is then bitwise XORed with the values obtained from the Duffing *** additional security,the XORed image is substituted through a dynamic *** evaluate the efficiency and performance of the proposed algorithm,several tests are performed which prove its resistance to various types of attacks such as brute-force and statistical attacks.

关键词： DNA sequence TD-ERCS chaoticmap henon chaoticmap duffing chaotic system SHA-512 encryption technique watermark embedding

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Fuzzy Logic-Based Deep Learning for Human-Machine Interaction and Gesture Recognition in Uncertain and Noisy Environments 1

Fuzzy Logic-Based Deep Learning for Human-Machine Interactio...

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1st International Conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Anand, John Thamilarasi, V. Rayal, Ashish Gupta, Himanshu Kumar Jyothi, Kamboji Vishwakarma, Pramatma Greater Noida201310 India Department of Computer Science & Engineering Tamilnadu Salem-16 India School of Applied and Life Sciences Uttaranchal University Department of Mathematics Dehradun248007 India Bipin Tripathi Kumaon Institute of Technology Department of Electrical Engineering Almora Dwarahat India Chaitanya Bharathi Institute of Technology Department of Mathematics AP Proddatur India Lovely Professional University Department of Computer Aplication Punjab 144411 India

ISBN: (纸本)9798331518578

Conventional deep learning methods have considerable hurdles when it comes to Human-Machine Interaction (HMI) and gesture identification in noisy and uncertain situations. A Fuzzy Logic-Based Deep Learning (Fuzzy DL) method is presented in this work to improve the resilience, accuracy, and adaptability of human gesture recognition in a variety of scenarios. We test several algorithms on various datasets, lighting situations, and noise levels, such as CNN, LSTM, Transformer, and Fuzzy DL. The outcomes show that Fuzzy DL functions better than other systems, retaining 91.0% accuracy at high noise levels and reaching 97.5% accuracy under typical circumstances. In comparison to traditional structures, it also shows faster inference times and a lower mean squared error. Its superiority is further demonstrated by comparative analysis on precision, recall, and F1-score. This study presents a robust and noise-resilient solution for real-world HMI applications, including as assistive systems and virtual interfaces. The outcomes indicate that incorporating fuzzy logic with DL substantially enhances gesture recognition in difficult environments. © 2025 IEEE.

关键词： Contrastive Learning

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Smart Hybrid Mini-Xception Model and Eyebrow Displacement-Based Stress Level and Emotion Detection System 12th

Smart Hybrid Mini-Xception Model and Eyebrow Displacement-Ba...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Bhuvana, S. Anantha Prabha, P. Deva Priya, M. Christy Jeba Malar, A. Karthick, S. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Tamilnadu Coimbatore India Department of Computer Science and Engineering Karpagam College of Engineering Tamilnadu Coimbatore India Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Delhi - NCR Campus SRM Institute of Science and Technology Uttar Pradesh Ghaziabad India

ISBN: (纸本)9789819789450

Stress is a state of mental or emotional strain due to adversative or challenging situations. A human may undergo bad life experiences or events, and it is a significant issue to be dealt in today's society. It comes as a result of family situations, health concerns, work environments and people's behavior among other things. It affects people of various ages, including children, teenagers, middle-aged people and senior citizens. It has serious consequences affecting human body and life. These problems can be addressed by detecting mental stress at an early stage. The main goal of the propounded system is to identify human stress using a pre-trained Mini-Xception model. Additional measures such as eye moments, mouth activity and head motion are employed for determining the level of human stress. FER2013 dataset is used and the system is tested for all kinds of emotions. Human stress level is experimented by capturing video using webcam. From the experimental outcomes, it is evident that the proposed system offers better performance in terms of Precision, Accuracy, Specificity and Sensitivity in contrast to traditional methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Eyebrow displacement FER2013 dataset Mini-Xception model Stress detection

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ANN-based analysis of thin film Maxwell fluid dynamics with electro-osmotic and nonlinear thermal effects

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Alexandria engineering Journal 2025年 127卷 392-410页

作者： Din, Irfan Saif Ud Siddique, Imran Zahid, Zohaib Nadeem, Muhammad Islam, S. Department of Mathematics University of Management and Technology Lahore54770 Pakistan Department of Mathematics University of Sargodha Sargodha40100 Pakistan Mathematics in Applied Sciences and Engineering Research Group Scientific Research Center Al-Ayen University Nasiriyah64001 Iraq Federal University of Technology Paran UTFPR Mechanical Engineering Department DAMEC Postgraduate Program in Mechanical and Materials Engineering PPGEM Research Center for Rheology and Non-Newtonian Fluids CERNN R. Deputado Heitor Alencar Furtado 5000 - Bloco N - Ecoville PR Curitiba81280-340 Brazil Department of Mechanical Engineering Prince Mohammad Bin Fahd University P.O Box 1664 Al Khobar31952 Saudi Arabia

An intelligent Levenberg-Marquardt Technique (LMT) with artificial neural network (ANN) backpropagation (BP) has been used to analyze the thermal heat and mass transfer of unsteady magnetohydrodynamics (MHD) thin film Maxwell fluid flow in a porous inclined sheet with an emphasis on the influence of electro-osmosis. The activation energy, chemical reaction, mixed convection, melting heat, joule heating, nonlinear thermal radiation, variable thermal conductivity and thermal source/sink effect are taken into account for transport expressions. Appropriate similarity transformations were used to translate partial differential equations (PDEs) into ordinary differential equations (ODEs). After that, the built-in MATLAB BVP4C method was used for a data set assessed using the LMT-ANN strategy to solve these ODEs. The physical significance of the designed parameters is thoroughly discussed in both tabular and graphical form. The observed R-squared value is 1, and the mean square error up to 10−15 demonstrates the LMT-ANN's precise and accurate computing capability. The model's validity is also confirmed by the strong agreement between the obtained predicted findings and numerical results, which shows a high degree of accuracy within the range of 10−8 to 10−11. It was revealed that radiative heat considerably increases surface heat energy through accumulation, improving heat transfer qualities, whereas fluid temperature is raised by Joule dissipation, variable thermal conductivity, and heat source. Electro-osmosis and magnetic fields reduce fluid velocity by generating opposing forces that resist the flow. This problem works best in microscale fluid transport systems and drilling operations, where magnetic and electro-osmotic control are crucial. These systems include micro-electromechanical systems, lab-on-a-chip devices, porous geological formations, and thin film coating technologies. © 2025 The Authors

关键词： Activation energy

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Prescribing Decision Conservativeness in Two-Stage Power Markets: A Distributionally Robust End-to-End Approach 59

Prescribing Decision Conservativeness in Two-Stage Power Mar...

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59th Annual Conference on Information Sciences and Systems, CISS 2025

作者： Liang, Zhirui Li, Qi Liu, Anqi Dvorkin, Yury Johns Hopkins University Department of Applied Mathematics and Statistics BaltimoreMD United States Johns Hopkins University Department of Computer Science BaltimoreMD United States Johns Hopkins University Department of Electrical and Computer Engineering BaltimoreMD United States Johns Hopkins University Department of Civil and System Engineering BaltimoreMD United States

ISBN: (纸本)9798331513269

This paper presents an end-to-end framework for calibrating wind power forecast models to minimize operational costs in two-stage power markets, where the first stage involves a distributionally robust optimal power flow (DR-OPF) model. Unlike traditional methods that adjust forecast parameters and uncertainty quantification (UQ) separately, this framework jointly optimizes both the forecast model parameters and the decision conservativeness, which determines the size of the ambiguity set in the DR-OPF model. The framework aligns UQ with actual uncertainty realizations by directly optimizing downstream operational costs, a process referred to as cost-oriented calibration. The calibration is achieved using a gradient descent approach. To enable efficient differentiation, the DR-OPF problem is reformulated into a convex form, and the Envelope Theorem is leveraged to simplify gradient derivation in the two-stage setting. Additionally, the framework supports distributed implementation, effectively reducing computational overhead and enhancing efficiency. By proactively calibrating forecast parameters and prescribing optimal decision conservativeness, the framework significantly enhances cost efficiency and reliability in power system operations. Numerical experiments on an IEEE 5-bus system demonstrate the effectiveness and efficiency of the proposed approach. © 2025 IEEE.

关键词： Wind forecasting

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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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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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