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RSC Pharmaceutics 2025年第2期2卷 353-368页

作者： Tamara Akpobolokemi Etelka Chung Rocio Teresa Martinez-Nunez Guogang Ren Bahijja Tolulope Raimi Abraham alex Griffiths King's College London School of Cancer and Pharmaceutical Sciences Institute of Pharmaceutical Science Waterloo Campus Franklin Wilkins Building Stamford Street London SE1 9NH UK bahijja.raimi-abraham@kcl.ac.uk King's College London Department of Infectious Diseases School of Immunology & Microbial Sciences Faculty of Life Sciences & Medicine King's College London Guy's Hospital London UK School of Physics Engineering and Computer Science University of Hertfordshire Hatfield AL10 9AB UK London Metallomics Facility Faculty of Life Sciences & Medicine King's College London London SE1 9NH UK alexander.1.griffiths@kcl.ac.uk

Plant mediated nanofabrication is a sustainable strategy for generating biocompatible nanomaterials with diverse industrial applications. Despite growing interest, there remain notable gaps in the understanding of the influence of plant extract concentration on the physiochemical properties of silver nanoparticles (AgNPs), particularly regarding their size. Conflicting reports suggest an increase in AgNP size with increased extract concentration, and others suggest the opposite. To address this, this study explores the influence of varying Spinacia oleracea ( S. oleracea ) leaf extract concentrations on the physiochemical properties of AgNPs and their antimicrobial activity against Gram negative ( Escherichia coli ), Gram positive ( Staphylococcus aureus, Streptococcus pyogenes ) bacteria and Fungi ( Candida albicans ). Hence, our investigation encompasses persistent infection-causing microorganisms currently plagued with drug resistance issues. This study's findings will enhance understanding of this sustainable nanofabrication approach, highlighting AgNP's potential application as novel antimicrobial agents. Results confirmed spherical nanoranged AgNPs were synthesised, obtaining AgNP-2%, AgNP-3%, AgNP-4%, AgNP-7%, and AgNP-10% v/v S. oleracea leaf extract. Our analysis revealed a consistent trend of size reduction with increasing extract concentration: AgNP-2% (173 nm), AgNP-3% (211 nm), AgNP-4% (148 nm), AgNP-7% (120 nm), and AgNP-10% (109 nm). Regarding antimicrobial activity, the lower concentration AgNPs (AgNP-2% and AgNP-3%) showed no activity, while all the higher concentrations AgNPs displayed full inhibition of all tested microbes. In summary, our research emphasises the significance of plant extract concentration in optimising AgNP synthesis and size reduction. The demonstrated antimicrobial properties suggest promising applications in industries such as environmental (water purification), biomedical (wound healing, drug delivery), and agricultural (pest

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Lag synchronization in an unidirectional ring of memristive neurons

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The European Physical Journal Special Topics 2025年 1-12页

作者： Vijayan, Vijeesh Natiq, Hayder Momani, Shaher Pham, Viet-Thanh Perc, Matjaž Center for Research SRM Easwari Engineering College Chennai India Center for Cognitive Science Trichy SRM Medical College Hospital and Research Center Trichy India Ministry of Higher Education and Scientific Research Baghdad Iraq Department of Computer Technology Engineering College of Information Technology Imam Ja’afar Al-Sadiq University Baghdad Iraq Nonlinear Dynamics Research Center (NDRC) Ajman University Ajman United Arab Emirates Department of Mathematics Faculty of Science University of Jordan Amman Jordan Faculty of Electronics Technology Industrial University of Ho Chi Minh City Ho Chi Minh City Vietnam Faculty of Natural Sciences and Mathematics University of Maribor Maribor Slovenia Community Healthcare Center Dr. Adolf Drolc Maribor Maribor Slovenia University College Korea University Seoul Republic of Korea

In recent years, the study of neuronal models has provided significant insights into brain dynamics and neurological disorders. Map-based neuronal models, such as the Rulkov map, have gained considerable popularity due to their computational efficiency and ability to replicate complex neuronal dynamics. We thus here study the collective dynamics of an unidirectional ring network composed of three memristive Rulkov maps, with particular emphasis on synchronization patterns and their dependence on coupling types. By employing electrical and memristive/field couplings, we analyze the emergence of complete synchronization, lag synchronization, and phase synchronization under varying coupling strengths. Our findings highlight how diffusive-based synaptic pathways modulate synchronization and collective behavior in the network. The presented results also offer new perspectives on the role of coupling functions in shaping neuronal synchronization, and they reveal their deeper implications for understanding pathological brain states and for designing neuromorphic systems.

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Satellite IOT Terminal Authentication Mechanism for Enhancing Consumer Security Over Cyber-Physical Systems

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IEEE Transactions on Consumer Electronics 2025年

作者： Ramesh, Janjhyam Venkata Naga Nimma, Divya Dasari, Rakeshnag alghamdi, Azzah Keshta, Ismail Tata, Ravi Kumar Soni, Mukesh Kiyosov, Sherzod Jangir, Pradeep Graphic Era Hill University Department of CSE Dehradun248002 India Graphic Era Deemed to Be University IndiaDepartment of CSE Uttarakhand Dehradun248002 India University of Southern Mississippi United States Acharya Nagarjuna University Department of CSE Nagarjuna Nagar Guntur522510 India Imam Abdalrhman Bin Faisal University College of Computer Science and Information Technology Computer Information Systems Department Khobar Saudi Arabia AlMaarefa University College of Applied Sciences Computer Science and Information Systems Department Riyadh Saudi Arabia Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Lovely Professional University Division of Research and Development Phagwara India Dr. D. Y. Patil Vidyapeeth Pune India Dr. D. Y. Patil School of Science and Technology Pune Tathawade India Tashkent State University of Economics The Department of Tax and Taxation Tashkent Uzbekistan Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biosciences Chennai602 105 India Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19328 Jordan

The satellite Internet of Things (IoT), utilizing Cyber-Physical Systems (CPS) Communication Networks and various satellite-borne and ground-based terminal devices, enables integrated human-object connectivity and information exchange across space, sky, and ground. A terminal authentication method based on trust evaluation is proposed to ensure the secure and efficient utilization of satellite IoT resources. This method optimizes authentication strategies by evaluating the trustworthiness of terminal devices. A satellite IoT terminal trust evaluation mechanism is proposed for scenarios where a previously strongly authenticated terminal needs re-authentication within a short time or where trust is granted via a strongly authenticated node. This mechanism describes dynamically adjusted trust metrics and evaluation models based on direct and indirect trust. A satellite IoT access authentication protocol based on trust evaluation is also designed, employing corresponding authentication mechanisms according to the device's trust level. This simplifies the re-authentication process for highly trusted devices, enabling differentiated authentication services for various terminal devices within the same authentication framework. A comprehensive security analysis of the protocol is conducted using informal methods and the Tamarin formal verification tool. Experiments validate the protocol's effectiveness, showing that it can reduce overall authentication costs and streamline the authentication process through trust-based simplifications. © 1975-2011 IEEE.

关键词： Authentication Protocol

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An Intelligent Framework for Recognizing Social Human-Object Interactions

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computers, Materials & Continua 2022年第10期73卷 1207-1223页

作者： Mohammed alarfaj Manahil Waheed Yazeed Yasin Ghadi Tamara al Shloul Suliman A.alsuhibany Ahmad Jalal Jeongmin Park Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Engineering Tech University of KoreaSiheung-si15073Gyeonggi-doKorea

Human object interaction(HOI)recognition plays an important role in the designing of surveillance and monitoring systems for healthcare,sports,education,and public *** involves localizing the human and object targets and then identifying the interactions between ***,it is a challenging task that highly depends on the extraction of robust and distinctive features from the targets and the use of fast and efficient ***,the proposed system offers an automated body-parts-based solution for HOI *** system uses RGB(red,green,blue)images as input and segments the desired parts of the images through a segmentation technique based on the watershed ***,a convex hullbased approach for extracting key body parts has also been *** identifying the key body parts,two types of features are ***,the entire feature vector is reduced using a dimensionality reduction technique called t-SNE(t-distributed stochastic neighbor embedding).Finally,a multinomial logistic regression classifier is utilized for identifying class labels.A large publicly available dataset,MPII(Max Planck Institute Informatics)Human Pose,has been used for system *** results prove the validity of the proposed system as it achieved 87.5%class recognition accuracy.

关键词： Dimensionality reduction human-object interaction key point detection machine learning watershed segmentation

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Optimization of Multi-Energy Storage in Urban Building Clusters

Results in Engineering

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Results in engineering 2025年

作者： Sameer algburi Aymen Mohammed Hassan Falah Fakhruldeen Ibrahim Abdullah Israa alhani ali Khudhair Qusay Hassan Michael Ssebunya Feryal Ibrahim Jabbar College of Engineering Al-Kitab University Kirkuk 36015 Iraq Electrical Technical College Al-Farahidi University Baghdad Iraq Computer Techniques Engineering Department Faculty of Information Technology Imam Ja’afar Al-Sadiq University Baghdad 10011 Iraq Computer Science Department Al-Turath University Baghdad Iraq Mazaya University College Iraq Department of Mechanical Engineering University of Deyala Deyala Iraq Medical Physics Department College of Sciences Al-Mustaqbal University 51001 Babil Iraq

A data-driven optimization framework was developed to enhance energy performance in building clusters through multi-energy storage systems, combining electrical and thermal solutions. The approach used surrogate modeling, symbolic regression, and genetic programming to simulate energy consumption, integrate weather and tariff data, and refine storage strategies across varied building types. Applied to a cluster in Bismayah city, Iraq, the methodology evaluated HVAC configurations, façade designs, and thermal mass levels to tailor storage capacity recommendations. Results revealed a 38% reduction in peak grid import and a 24.6% drop in overall energy consumption when hybrid energy storage was implemented. A thermal energy storage tank capacity of 4651 kWh, coupled with a battery storage unit of 342 kWh, demonstrated a 35% decrease in energy costs. Demand response participation increased by 45% through strategic use of pre-cooling routines and temperature reset controls. TES-only configurations achieved energy usage of 16.3 kWh/m², while hybrid configurations further reduced to 8.7 kWh/m². Budget analyses showed that investments ranging from $2.6 million to $10.4 million proportionally enhanced system performance without over-sizing. The integration of Battery and Thermal Energy Storage with Phase Change Materials further supported passive thermal control, reducing HVAC reliance during peak hours.

关键词： Energy management Storage systems Optimization Demand response Efficiency

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Blockchain-Assisted Trust Framework for Increased Survivability in Internet of Vehicles

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Internet Technology Letters 2025年第3期8卷

作者： Sharma, Kanta Prasad al-Rashed, Atheer Johar, M.G.M. alsoud, Anas Ratib Singh, Amrita Dey, Protyay Pant, Shivani Amity University Uttar Pradesh Greater Noida India College of Administrative Sciences Applied Science University Al Eker Bahrain Management and Science University Selangor Shah Alam Malaysia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Department of Computer Science and Engineering Chandigarh Engineering College Chandigarh Group of Colleges-Jhanjeri Mohali India NIMS School of Computing Science and Artificial Intelligence NIMS University Rajasthan Jaipur India Uttaranchal Institute of Technology Uttaranchal University Dehradun India

VANETs enhance traffic efficiency and road safety, but they can be attacked by malicious vehicles. These malicious vehicles can cause accidents or endanger lives by broadcasting false event messages and disrupting Internet of Vehicles applications. Before responding to sender messages, receiver vehicles must estimate the legitimacy and trustworthiness of the source vehicles. Existing solutions struggle to balance security and efficiency effectively. This paper introduces a model that combines the advantages of blockchain technology and trust model models to improve the trustworthiness, efficacy, and security of vehicular networks, alongside secure trust-based optimized routing. Extensive experiments demonstrate the security and efficiency of the proposed model. The proposed model is adaptable to diverse VANET scenarios, addressing all security and privacy needs more comprehensively than current trust schemes. Efficiency analysis and simulation results show that our proposed framework outperforms baseline models, highlighting its security, effectiveness, and robustness in enhancing IoV communication security. © 2025 John Wiley & Sons Ltd.

关键词： Highway accidents

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Optimal deep learning based vehicle detection and classification using chaotic equilibrium optimization algorithm in remote sensing imagery

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Scientific reports 2025年第1期15卷 17921页

作者： Youseef alotaibi Krishnaraj Nagappan Tamilvizhi Thanarajan Surendran Rajendran Department of Software Engineering College of Computing Umm Al-Qura University Makkah 21955 Saudi Arabia. Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu 603203 India. Department of Computer Science and Engineering Panimalar Engineering College Chennai 600123 India. tamilvizhi.phd.it@***. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India.

Remote sensing images (RSI), such as aerial or satellite images, produce a large-scale view of the Earth's surface, which gets them used to track and monitor vehicles from several settings, like border control, disaster response, and urban traffic surveillance. Vehicle detection and classification using RSIs is a vital application of computer vision and image processing. It contains locating and identifying vehicles from the image. It is done using many approaches that have object detection approaches, namely YOLO, Faster R-CNN, or SSD, which utilize deep learning (DL) to locate and identify the image. Additionally, the classification of vehicles from RSIs contains classification of them based on their variety, such as trucks, motorcycles, cars or buses, utilizing machine learning (ML) techniques. This article designed and developed an automated vehicle type detection and classification using a chaotic equilibrium optimization algorithm with deep learning (VDTC-CEOADL) on high-resolution RSIs. The VDTC-CEOADL technique presented examines high-quality RSIs for the accurate detection and classification of vehicles. The VDTC-CEOADL technique employs a YOLO-HR object detector with a residual network as the backbone model to accomplish this. In addition, CEOA based hyperparameter optimizer is designed for the parameter tuning of the ResNet model. For the vehicle classification process, the VDTC-CEOADL technique exploits the attention-based long-short-term memory (alSTM) mod-el. Performance validation of the VDTC-CEOADL technique is validated on a high-resolution RSI dataset, and the results portrayed the supremacy of the VDTC-CEOADL technique in terms of different measures.

关键词： Chaotic equilibrium optimization algorithm Deep learning Remote sensing images Vehicle classification Vehicle detection

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Denoising Medical Images Using Deep Learning in IoT Environment

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computers, Materials & Continua 2021年第12期69卷 3127-3143页

作者： Sujeet More Jimmy Singla Oh-Young Song Usman Tariq Sharaf Malebary School of Computer Science and Engineering Lovely Professional UniversityJalandharIndia Software Department Sejong UniversitySeoulKorea College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology in RabighKing Abdulaziz UniversityJeddah21911Saudi Arabia

Medical Resonance Imaging(MRI)is a noninvasive,nonradioactive,and meticulous diagnostic modality capability in the field of medical ***,the efficiency of MR image reconstruction is affected by its bulky image sets and slow process ***,to obtain a high-quality reconstructed image we presented a sparse aware noise removal technique that uses convolution neural network(SANR_CNN)for eliminating noise and improving the MR image reconstruction *** proposed noise removal or denoising technique adopts a fast CNN architecture that aids in training larger datasets with improved quality,and SARN algorithm is used for building a dictionary learning technique for denoising large image *** proposed SANR_CNN model also preserves the details and edges in the image during *** experiment was conducted to analyze the performance of SANR_CNN in a few existing models in regard with peak signal-to-noise ratio(PSNR),structural similarity index(SSIM),and mean squared error(MSE).The proposed SANR_CNN model achieved higher PSNR,SSIM,and MSE efficiency than the other noise removal *** proposed architecture also provides transmission of these denoised medical images through secured IoT architecture.

关键词： Medical resonance imaging convolutional neural network denoising contrast enhancement internet of things rheumatoid arthritis

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Arithmetic Optimization with Ensemble Deep Transfer Learning Based Melanoma Classification

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computers, Materials & Continua 2023年第4期75卷 149-164页

作者： K.Kalyani Sara A althubiti Mohammed altaf Ahmed ELaxmi Lydia Seifedine Kadry Neunggyu Han Yunyoung Nam Department of Computer Science Dr.Nalli Kuppusamy Arts College(Affiliated to Bharathidasan UniversityTiruchirappalli)Thanjavur613003India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Lepartment of Computer Engineering College of Computer Engineering&SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Department of ICT Convergence Soonchunhyang UniversityKorea

Melanoma is a skin disease with high mortality rate while earlydiagnoses of the disease can increase the survival chances of patients. Itis challenging to automatically diagnose melanoma from dermoscopic skinsamples. computer-Aided Diagnostic (CAD) tool saves time and effort indiagnosing melanoma compared to existing medical approaches. In this background,there is a need exists to design an automated classification modelfor melanoma that can utilize deep and rich feature datasets of an imagefor disease classification. The current study develops an Intelligent ArithmeticOptimization with Ensemble Deep Transfer Learning Based MelanomaClassification (IAOEDTT-MC) model. The proposed IAOEDTT-MC modelfocuses on identification and classification of melanoma from dermoscopicimages. To accomplish this, IAOEDTT-MC model applies image preprocessingat the initial stage in which Gabor Filtering (GF) technique is *** addition, U-Net segmentation approach is employed to segment the lesionregions in dermoscopic images. Besides, an ensemble of DL models includingResNet50 and ElasticNet models is applied in this study. Moreover, AOalgorithm with Gated Recurrent Unit (GRU) method is utilized for identificationand classification of melanoma. The proposed IAOEDTT-MC methodwas experimentally validated with the help of benchmark datasets and theproposed model attained maximum accuracy of 92.09% on ISIC 2017 dataset.

关键词： Skin cancer deep learning melanoma classification dermoscopy computer aided diagnosis

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An Effective Hybrid Model of ELM and Enhanced GWO for Estimating Compressive Strength of Metakaolin-Contained Cemented Materials

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computer Modeling in engineering & sciences 2024年第5期139卷 1521-1555页

作者： Abidhan Bardhan Raushan Kumar Singh Mohammed alatiyyah Sulaiman Abdullah alateyah Department of Civil Engineering National Institute of Technology PatnaPatnaIndia Department of Computer Engineering and Applications GLA UniversityMathuraIndia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Science College of Science and ArtsQassim UniversityUnaizahSaudi Arabia

This research proposes a highly effective soft computing paradigm for estimating the compressive strength(CS)of metakaolin-contained cemented *** proposed approach is a combination of an enhanced grey wolf optimizer(EGWO)and an extreme learning machine(ELM).EGWO is an augmented form of the classic grey wolf optimizer(GWO).Compared to standard GWO,EGWO has a better hunting mechanism and produces an optimal *** EGWO was used to optimize the ELM structure and a hybrid model,ELM-EGWO,was *** train and validate the proposed ELM-EGWO model,a sum of 361 experimental results featuring five influencing factors was *** on sensitivity analysis,three distinct cases of influencing parameters were considered to investigate the effect of influencing factors on predictive *** consequences show that the constructed ELM-EGWO achieved the most accurate precision in both training(RMSE=0.0959)and testing(RMSE=0.0912)*** outcomes of the ELM-EGWO are significantly superior to those of deep neural networks(DNN),k-nearest neighbors(KNN),long short-term memory(LSTM),and other hybrid ELMs constructed with GWO,particle swarm optimization(PSO),harris hawks optimization(HHO),salp swarm algorithm(SSA),marine predators algorithm(MPA),and colony predation algorithm(CPA).The overall results demonstrate that the newly suggested ELM-EGWO has the potential to estimate the CS of metakaolin-contained cemented materials with a high degree of precision and robustness.

关键词： Metakaolin-contained cemented materials compressive strength extreme learning machine grey wolf optimizer swarm intelligence uncertainty analysis artificial intelligence

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