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An Enhanced Lung Cancer Detection Approach Using Dual-Model Deep Learning Technique

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 835-867页

作者： Sumaia Mohamed Elhassan saad Mohamed Darwish Saleh Mesbah Elkaffas Department of Information Technology Institute of Graduate Studies and ResearchAlexandria UniversityAlexandria21526Egypt College of Computing and Information Technology Arab Academy for ScienceTechnology and Maritime TransportAlexandriaP.O.Box 1029Egypt

Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic *** detection of lung tumors significantly increases the chances of successful treatment and ***,current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung ***-model deep learning technologies for lung cancer detection,while beneficial,cannot capture the full range of features present in medical imaging data,leading to incomplete or inaccurate ***,it may not be robust enough to handle the wide variability in medical images due to different imaging conditions,patient anatomy,and tumor *** overcome these disadvantages,dual-model or multi-model approaches can be *** research focuses on enhancing the detection of lung cancer by utilizing a combination of two learning models:a Convolutional Neural Network(CNN)for categorization and the You Only Look Once(YOLOv8)architecture for real-time identification and pinpointing of *** automatically learn to extract hierarchical features from raw image data,capturing patterns such as edges,textures,and complex structures that are crucial for identifying lung ***8 incorporates multiscale feature extraction,enabling the detection of tumors of varying sizes and scales within a single *** is particularly beneficial for identifying small or irregularly shaped tumors that may be challenging to ***,through the utilization of cutting-edge data augmentation methods,such as Deep Convolutional Generative Adversarial Networks(DCGAN),the suggested approach can handle the issue of limited data and boost the models’ability to learn from diverse and comprehensive *** combined method not only improved accuracy and localization but also ensured efficient real-time processing,which is crucial for practical clinical applicati

关键词： Lung cancer detection dual-model deep learning technique data augmentation CNN YOLOv8

Enhancing Deepfake Detection:Proactive Forensics Techniques Using Digital Watermarking

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Computers, Materials & Continua 2025年第1期82卷 73-102页

作者： Zhimao Lai saad Arif Cong Feng Guangjun Liao Chuntao Wang School of Automation Guangdong University and TechnologyGuangzhou510006China School of Immigration Administration(Guangzhou) China People’s Police UniversityGuangzhou510663China Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl Ahsa31982Saudi Arabia Cyber Police Division Guangzhou Public Security BureauGuangzhou510030China School of Forensic Science and Technology Guangdong Police CollegeGuangzhou510320China College of Mathematics and Informatics South China Agricultural UniversityGuangzhou510642China

With the rapid advancement of visual generative models such as Generative Adversarial Networks(GANs)and stable Diffusion,the creation of highly realistic Deepfake through automated forgery has significantly *** paper examines the advancements inDeepfake detection and defense technologies,emphasizing the shift from passive detection methods to proactive digital watermarking *** detection methods,which involve extracting features from images or videos to identify forgeries,encounter challenges such as poor performance against unknown manipulation techniques and susceptibility to counter-forensic *** contrast,proactive digital watermarking techniques embed specificmarkers into images or videos,facilitating real-time detection and traceability,thereby providing a preemptive defense againstDeepfake *** offer a comprehensive analysis of digitalwatermarking-based forensic techniques,discussing their advantages over passivemethods and highlighting four key benefits:real-time detection,embedded defense,resistance to tampering,and provision of legal ***,the paper identifies gaps in the literature concerning proactive forensic techniques and suggests future research directions,including cross-domain watermarking and adaptive watermarking *** systematically classifying and comparing existing techniques,this review aims to contribute valuable insights for the development of more effective proactive defense strategies in Deepfake forensics.

关键词： Deepfake proactive forensics digital watermarking traceability detection techniques

Global Piecewise Analysis of HIV Model with Bi-Infectious Categories under Ordinary Derivative and Non-Singular Operator with Neural Network Approach

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 609-633页

作者： Ghaliah Alhamzi Badr saad TAlkahtani Ravi Shanker Dubey Mati ur Rahman Department of Mathematics and Statistics College of ScienceImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh13318Saudi Arabia Department of Mathematics College of ScienceKing Saud UniversityRiyadh11989Saudi Arabia Department of Mathematics Amity School of Applied SciencesAmity University RajasthanJaipur302002India School of Mathematical Sciences Jiangsu UniversityZhenjiang212013China Department of Computer Science and Mathematics Lebanese American UniversityBeirut13-5053Lebanon

This study directs the discussion of HIV disease with a novel kind of complex dynamical generalized and piecewise operator in the sense of classical and Atangana Baleanu(AB)derivatives having arbitrary *** HIV infection model has a susceptible class,a recovered class,along with a case of infection divided into three sub-different levels or categories and the recovered *** total time interval is converted into two,which are further investigated for ordinary and fractional order operators of the AB derivative,*** proposed model is tested separately for unique solutions and existence on bi *** numerical solution of the proposed model is treated by the piece-wise numerical iterative scheme of Newtons *** proposed method is established for piece-wise derivatives under natural order and non-singular Mittag-Leffler *** cross-over or bending characteristics in the dynamical system of HIV are easily examined by the aspect of this research having a memory effect for controlling the said *** study uses the neural network(NN)technique to obtain a better set of weights with low residual errors,and the epochs number is considered *** obtained figures represent the approximate solution and absolute error which are tested with NN to train the data accurately.

关键词： HIV infection model qualitative scheme approximate solution piecewise global operator neural network

Impact of Pollutant Concentration and Particle Deposition on the Radiative Flow of Casson-Micropolar Fluid between Parallel Plates

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 665-690页

作者： Ghaliah Alhamzi Badr saad T.Alkahtani Ravi Shanker Dubey Vinutha kalleshachar Neelima Nizampatnam Department of Mathematics and Statistics College of ScienceImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11564Saudi Arabia Department of Mathematics College of ScienceKing Saud UniversityRiyadh11989Saudi Arabia Department of Mathematics Amity School of Applied SciencesAmity University RajasthanJaipur302002RajasthanIndia Department of Studies in Mathematics Davangere UniversityDavangere577002India Department of Electronics and Communication Engineering Amrita School of EngineeringAmrita Vishwa VidyapeethamBengaluru560035India

Assessing the behaviour and concentration of waste pollutants deposited between two parallel plates is essential for effective environmental *** the effectiveness of treatment methods in reducing pollution scales is made easier by analysing waste discharge *** waste discharge concentration analysis is useful for assessing how effectively wastewater treatment techniques reduce pollution *** study aims to explore the Casson micropolar fluid flow through two parallel plates with the influence of pollutant concentration and thermophoretic particle *** explore the mass and heat transport features,thermophoretic particle deposition and thermal radiation are *** governing equations are transformed into ordinary differential equations with the help of suitable similarity *** Runge-kutta-Fehlberg’s fourthfifth order technique and shooting procedure are used to solve the reduced set of equations and boundary *** integration of a neural network model based on the Levenberg-Marquardt algorithm serves to improve the accuracy of predictions and optimize the analysis of *** outcomes are displayed to analyze the characteristics of the relevant dimensionless parameters in the current *** reveal that concentration upsurges as the micropolar parameter *** concentration reduces with an upsurge in the thermophoretic *** upsurge in the external pollutant source variation and the local pollutant external source parameters enhances mass *** surface drag force declines for improved values of porosity and micropolar parameters.

关键词： Micropolar fluid thermal radiation porous medium thermophoretic particle deposition waste discharge concentration

Oversampling-Enhanced Feature Fusion-Based Hybrid ViT-1DCNN Model for Ransomware Cyber Attack Detection

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Computer Modeling in Engineering & Sciences 2025年第2期142卷 1667-1695页

作者： Muhammad Armghan Latif Zohaib Mushtaq Saifur Rahman saad Arif Salim Nasar Faraj Mursal Muhammad Irfan Haris Aziz Department of Computer and Information System Cleveland State UniversityOhio44115USA Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl Ahsa31982Saudi Arabia Department of Mechanical Industrial and Energy System EngineeringUniversity of SargodhaSargodha40100Pakistan

Ransomware attacks pose a significant threat to critical infrastructures,demanding robust detection *** study introduces a hybrid model that combines vision transformer(ViT)and one-dimensional convolutional neural network(1DCNN)architectures to enhance ransomware detection *** common challenges in ransomware detection,particularly dataset class imbalance,the synthetic minority oversampling technique(SMOTE)is employed to generate synthetic samples for minority class,thereby improving detection *** integration of ViT and 1DCNN through feature fusion enables the model to capture both global contextual and local sequential features,resulting in comprehensive ransomware *** on the UNSW-NB15 dataset,the proposed ViT-1DCNN model achieved 98%detection accuracy with precision,recall,and F1-score metrics surpassing conventional *** approach not only reduces false positives and negatives but also offers scalability and robustness for real-world cybersecurity *** results demonstrate the model’s potential as an effective tool for proactive ransomware detection,especially in environments where evolving threats require adaptable and high-accuracy solutions.

关键词： Ransomware attacks cybersecurity vision transformer convolutional neural network feature fusion encryption threat detection

Insight into physical properties of lutetium-based double half-Heusler alloys LuXCo_(2)Bi_(2)(X=V,Nb and Ta)

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Journal of Rare Earths 2025年第1期43卷 199-208,I0008页

作者： Saber saad Essaoud Said Al Azar Ahmad A.Mousa Anas Y.Al-Reyahi Nabil Al Aqtash Mohammed Elamin ketfi Department of Physics Faculty of ScienceUniversity of M'sila28000M'silaAlgeria Laboratoire de Physique des Particules et Physique Statistique Ecole Normale Superieure-KoubaBP 92Vieux-Kouba16050AlgiersAlgeria Department of Physics Faculty of ScienceZarqa UniversityZarqa13132Jordan Department of Basic Sciences Middle East UniversityAmman183Jordan Applied Science Research Center Applied Science Private UniversityAmman11937Jordan Department of Physics Faculty of ScienceThe Hashemite UniversityP.0.Box 330127Zarqa13133Jordan Department of Electronics Faculty of TechnologyUniversity of M'sila28000M'silaAlgeria

Based on the density functional theory,the double half-Heusler alloys LuXCo_(2)Bi_(2)(X=V,Nb,and Ta)were studied to predict their structural,thermodynamic,thermoelectric,and optical *** the considered alloys are thermodynamically stable and have semiconductor behavior with indirect band gaps of 0.62,0.75,and 0.8 eV for LuVCo_(2)Bi_(2),LuNbCo_(2)Bi_(2),and LuTaCu_(2)Bi_(2),*** investigated compounds exhibit semiconducting behavior with energy gaps below 0.8 *** impact of heat and pressure on thermodynamic coefficients was evaluated,and the influence of charge carriers on the temperature-dependent properties was studied using the semi-classical Boltzmann *** studied compounds were characterized by their low lattice thermal conductivity at room temperature and low thermal expansion *** alloys exhibit substantial absorption coefficients in the ultraviolet(UV)light region,high optical conductivity,and high reflectivity in the visible light region,making them highly appealing materials for applications in the energy and electronics sectors.

关键词： Double half-Heusler Abinitio calculation Optical coefficients Thermoelectricity Lattice thermal conductivity Rareearths

Digital Innovation in Oral Health Care: A Comprehensive Review

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Open Journal of Stomatology 2025年第1期15卷 1-24页

作者： Abir Eddhaoui Tarek Ezzat Aly saad Haroon Department of Dental Primary Health Care Corporation Doha Qatar

The integration of digital technologies into oral health care is transforming the field, driving advancements in diagnostic precision, patient engagement, and access to care. This review evaluates the impact of mobile health (mHealth) applications, tele-dentistry, artificial intelligence (AI), wearable devices, and advanced imaging systems on modern dentistry. Synthesizing findings from 125 studies published between 2010 and 2024, the paper identifies key achievements, including improved patient compliance, enhanced diagnostic capabilities, and expanded access to care in underserved areas. Tele-dentistry has been pivotal in bridging geographical gaps, particularly during the COVID-19 pandemic, while AI tools have revolutionized diagnostics and personalized treatment planning. Wearable devices and mHealth applications have empowered patients with real-time feedback, fostering sustained adherence to oral hygiene practices. The review also highlights the potential of digital tools to reduce healthcare disparities and operational costs, paving the way for more equitable and efficient dental care. By outlining critical advancements and future directions, this paper underscores the transformative potential of digital health technologies in dentistry. It advocates for further research on data security, interoperability, and innovative applications of AI to maximize the benefits of these tools, ultimately shaping a more accessible and patient-focused oral health ecosystem.

关键词： Digital Dentistry Tele-Dentistry Artificial Intelligence Mobile Health Wearable Technology Diagnostic Innovation Oral Health Care

Predictors of Successful Radiofrequency Ablation of Benign Thyroid Nodules: A Single Centre Analysis

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International Journal of Clinical Medicine 2025年第1期16卷 16-28页

作者： Waswa Edgar Jiangyuan Cheng Esraa Atef saad Mwale Paul Wu Meng Department of General and Interventional Ultrasound Zhongnan Hospital of Wuhan University Wuhan China

Objective: To assess the predictors of successful inactivation of benign thyroid nodules using radiofrequency ablation (RFA) and the hormonal responses thereafter. Methods: A retrospective study conducted at Zhongnan Hospital of Wuhan University (January 2022 to January 2024) analysed thyroid tumor characteristics using B-mode ultrasound, colour Doppler imaging, and CEUS post-RFA. Thyroid hormone levels were also assessed before RFA and at 1, 3, and 6 months after the procedure. Results: The study involved 72 patients with benign thyroid nodules, comprising 13 males and 59 females, with a mean age of 45.8 ± 12.1 years. Complete inactivation was achieved in 70.8% of nodules, while 29.2% showed partial inactivation. Nodules with complete inactivation exhibited more calcification (p = 0.040), whereas those with partial inactivation demonstrated higher vascularity (p Conclusion: In conclusion, this study found that therapeutic RFA effectively achieves high rates of complete inactivation in benign thyroid nodules, with the degree of inactivation mainly influenced by nodule vascularity and calcifications.

关键词： Predictors Radiofrequency Ablation Benign Thyroid Nodules

Predicting peak ground acceleration using the ConvMixer network

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Earthquake Science 2025年第2期38卷 126-135页

作者： Mohammed, Mona saad, Omar M. keshk, Arabi Ahmed, Hatem M. National Research Institute of Astronomy and Geophysics (NRIAG) Cairo 11511 Egypt Division of Physical Science and Engineering King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Delta Technological University Menoufia 32684 Egypt Faculty of Computers and Information Menoufia University Menoufia 32512 Egypt

The level of ground shaking, as determined by the peak ground acceleration (PGA), can be used to analyze seismic hazard at a certain location and is crucial for constructing earthquake-resistant structures. Predicting the PGA immediately after an earthquake occurs allows for the issuing of a warning by an earthquake early warning system. In this study, we propose a deep learning model, ConvMixer, to predict the PGA recorded by weak-motion velocity seismometers in Japan. We use 5-s three-component seismograms, from 2 s before until 3 s after the P-wave arrival time of the earthquake. Our dataset comprised more than 50,000 single-station waveforms recorded by 10 seismic stations in the k-NET, kiki-NET, and Hi-Net networks between 2004 and 2023. The proposed ConvMixer is a patch-based model that extracts global features from input seismic data and predicts the PGA of an earthquake by combining depth and pointwise convolutions. The proposed ConvMixer network had a mean absolute error of 2.143 when applied to the test set and outperformed benchmark deep learning models. In addition, the proposed ConvMixer demonstrated the ability to predict the PGA at the corresponding station site based on 1-second waveforms obtained immediately after the arrival time of the P-wave. © 2025 The Authors

关键词： deep learning (DL) earthquake early warning system (EEWS) peak ground acceleration (PGA)