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Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： Asma Anjum Dattathreya Salman Arafath Mohammed Dhaval Rabadiya K. B. Venkata Brahma Rao Ramya Maranan Department of Computer Science and Engineering HKBK College of Engineering Bengaluru Karnataka Department of Electronics and Communication Engineering Alva’s Institute of Engineering and Technology Moodbidri Karnataka Department of Electrical Engineering Computer Engineering Section College of Engineering King Khalid University Abha Asir Region Saudi Arabia Faculty of Management Studies Parul University Dist. Vadodara Gujarat Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

The fundamental requirement for WSNs made up of power-efficient and inexpensive sensor nodes depends on short-distance multi-hop communication. The potential of WSNs expands through new digital electronics and wireless communications technology despite the ongoing critical challenge related to energy efficiency. The main energy issues in WSNs stem from the excessively high control signal overhead between base stations and sensor nodes together with packet losses that require retransmissions to reduce energy efficiency. The proposed routing technique solves energy inefficiencies by employing RBDN-HMOA as an energy-efficient routing method. The proposed approach lowers system power consumption through its algorithm which picks distance and energy residual-based optimum forwarding nodes and adapts sampling rates automatically. The algorithm adopts Human Memory Optimization Algorithm (HMOA) to optimize network parameters. Research data shows that experimental results demonstrate both power efficiency and enhanced data transfer rate. The network demonstrates scalability because both throughputs reach 1100 kbps and energy usage reduces to 0.08 J when using 10 nodes. The RBDN-HMOA creates a practical method which extends network duration in practical deployments by diminishing WSN energy utilization without compromising data integrity.

关键词： Wireless sensor networks Energy consumption Fluctuations Heuristic algorithms Throughput Routing Energy efficiency Real-time systems Delays Optimization

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DRacv: Detecting and auto-repairing vulnerabilities in role-based access control in web application

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Journal of Network and computer Applications 2025年 240卷

作者： Xu, Ke Zhang, Bing Li, Jingyue He, Haitao Ren, Rong Ren, Jiadong School of Information Science and Engineering Yanshan University No. 438 West Section of Hebei Street Hebei Qinhuangdao06600 China The Department of Computer science Norwegian University of Science and Technology Trondheim 7030 Norway The Key Laboratory for Software Engineering of Hebei Province Yanshan University Hebei Qinhuangdao06600 China

Traditional methods for analyzing Broken Access Control (BAC) vulnerabilities have limitations regarding low coverage of access control rules, high false positive rate (FPR). Additionally, state-of-the-art strategies for repairing BAC vulnerabilities utilizing statement-level replacement as a repair method may introduce new logical errors. To address these challenges, we propose a novel approach called DRacv (Detect and Repair Access Control Vulnerabilities) to identify and auto-repair vulnerabilities in Role-Based Access Control (RBAC) mode used in web applications. To detect vulnerabilities, DRacv first constructs a Fine-grained Global Multi-attribute Architectural Navigation Graph model (FG-MANG) for web applications through dynamic execution and static analysis, which characterizes full relationships between roles, privileges, and accessible page resources. Based on access control rules extracted from FG-MANG, DRacv generates targeted attack payloads to detect BAC vulnerabilities, significantly reducing FPR and eliminating redundant attack payloads. To auto-repair the identified vulnerabilities, DRacv first precisely extracts access control privilege parameters, validation functions, and contextual statements to construct the patch code templates. These templates generate user- and role-level verification patch codes for different users and roles. Instead of changing the vulnerable code, the patch codes behave like firewalls. They are added as separate files and invoked by the web page with vulnerability to defend against access control compromises. DRacv was evaluated on 12 popular open-source web applications in PHP and JAVA. From the applications, DRacv identified 35 vulnerabilities (11 were new) with only one false positive, achieving an FPR of 2.78%. We also compared DRacv's detection results with state-of-the-art studies. Results show that DRacv outperforms those studies regarding the number of vulnerabilities detected and FPR. Among the 35 vulnerabilities

关键词： Open source software

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Quantum nonlocal modulation cancelation with distributed clocks

Optica Quantum

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Optica Quantum 2025年第1期3卷 45-54页

作者： Stephen D Chapman Suparna Seshadri Joseph M Lukens Nicholas A Peters Jason D McKinney Andrew M Weiner Hsuan-Hao Lu Elmore Family School of Electrical and Computer Engineering and Purdue Quantum Science and Engineering Institute Purdue University West Lafayette Indiana 47907 USA Research Technology Office and Quantum Collaborative Arizona State University Tempe Arizona 85287 USA Quantum Information Science Section Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA

We demonstrate nonlocal modulation of entangled photons with truly distributed radio frequency (RF) clocks. Leveraging a custom radio-over-fiber (RFoF) system characterized via classical spectral interference, we validate its effectiveness for quantum networking by multiplexing the RFoF clock with one photon from a frequency-bin-entangled pair and distributing the coexisting quantum-classical signals over fiber. Phase modulation of the two photons reveals nonlocal correlations in excellent agreement with theory: in-phase modulation produces additional sidebands in the joint spectral intensity, while out-of-phase modulation is nonlocally canceled. Our simple, feedback-free design attains subpicosecond synchronization—namely, drift less than ∼0.5 ps in a 5.5 km fiber over 30 min (fractionally only ∼2×10 −8 of the total fiber delay)—and should facilitate frequency-encoded quantum networking protocols such as high-dimensional quantum key distribution and entanglement swapping, unlocking frequency-bin qubits for practical quantum communications in deployed metropolitan-scale networks.

关键词： Entanglement distribution Laser communications Phase modulation Quantum communications Quantum key distribution Quantum teleportation

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Advancing Blackboard Framework: Responsive Trigger Mechanisms and Dynamic Feasibility Conditions for Optimal Knowledge Source Selection

Advancing Blackboard Framework: Responsive Trigger Mechanism...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Hana Munira Muhd Mukhtar Husna Sarirah Husin Suriana Ismail Azizah Rahmat Roslan Ismail Software Engineering Section Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia School of Computer Science Taylor's University Kuala Lumpur Malaysia Centre of Innovative Digital Education & Emerging Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

Expert systems (ES) rely heavily on effective control mechanisms to navigate complex problem-solving tasks. These mechanisms govern how ES utilizes knowledge and data to make decisions and execute actions. Optimizing these mechanisms, or addressing the “control problem,” remains a central challenge. Blackboard architectures, with their separation of knowledge and control processes, offer a promising approach. This study introduces an improved control mechanism for blackboard architectures. It focuses on a critical aspect-trigger mechanisms and feasible conditions-for optimal knowledge source selection to participate in the problem-solving process. By leveraging these features, this study aims to enhance decision-making and problem-solving capabilities within complex domains. This paper focuses on the technical details of exploring how it manages the flow of rule activation and execution throughout the problem-solving process.

关键词： Navigation Decision making Process control Information management Problem-solving Expert systems

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Developing a practical model for noise in entangled photon detection

arXiv

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arXiv 2025年

作者： Truong, Taman Arenz, Christian Lukens, Joseph M. School of Electrical Computer and Energy Engineering and Research Technology Office Arizona State University TempeAZ85287 United States Elmore Family School of Electrical and Computer Engineering Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Quantum Information Science Section Oak Ridge National Laboratory Oak RidgeTN37831 United States

We develop a comprehensive model for the effective two-photon density matrix produced by a parametric source of entangled photon pairs under a variety of detector configurations commonly seen in a laboratory setting: two and four photon number-resolving (PNR) and threshold detectors. We derive the probability of obtaining a single coincidence assuming Poisson-distributed photon pairs, non-unit detection efficiency, and dark counts;obtain the effective density matrix;and use this quantity to compute the fidelity of the generated quantum state. The 4 PNR case admits an analytic result valid for any combination of parameters, while all other cases leverage low-efficiency approximations to arrive at closed-form expressions. Interestingly, our model reveals appreciable fidelity improvements from four detectors as opposed to two yet minimal advantages for PNR over threshold detectors in the regimes explored. Overall, our work provides a valuable tool for the quantitative design of two-photon experiments under realistic nonidealities. Copyright © 2025, The Authors. All rights reserved.

关键词： Quantum entanglement

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Novel intelligent exogenous neuro-architecture–driven machine learning approach for nonlinear fractional breast cancer risk system

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Communications in Nonlinear Science and Numerical Simulation 2025年 149卷

作者： Fida, Afshan Raja, Muhammad Asif Zahoor Chang, Chuan-Yu Raja, Muhammad Junaid Ali Asif Khan, Zeshan Aslam Shoaib, Muhammad of Engineering Science and Technology National Yunlin University of Science and Technology Yunlin 64002 Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin 64002 Taiwan International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan AI center Yuan Ze University Taoyuan 320 Taiwan

Breast cancer remains one of the most prevalent and life-threatening diseases worldwide, necessitating mathematical modelling frameworks to capture the complexity of its progression and risk factors. This research endeavor uncovers the novel machine learning expedition using an Adaptive Nonlinear AutoRegressive eXogenous (ANARX) neural network on a Fractional Order Breast Cancer Risk (FO-BCR) model. A novel Caputo fractional operator-based breast cancer risk model is presented using a five compartmental system reflected by healthy, tumor, immune, estrogen, and fatty cells. A modified fractional Adams PECE method is opted to generate solutions of the five fractional order variants on the four diverse BCR scenarios. These temporal sequences are parsed as ground truth for the adept ANARX network, which is iteratively refined using the Levenberg-Marquardt (LM) algorithm. The performance evaluation of the temporal feature learning of the ANARX-LM algorithm is comprehensively evaluated against reference numerical outcomes using mean square error (MSE) performance graphics, input-error cross correlation, error autocorrelation, error histogram analysis, sequential response and comparative error analysis charts. Low disparity between reference solutions is observed for all FO-BCR systems, with MSE errors in the range of 10−8 to 10−11. Finally, the ANARX-LM's predictive prowess is evaluated using the single and multistep configurations. Minute errors in the range of 10−9 to 10−11, 10−8 to 10−10 suggest accurate anticipation of the FO-BCR system enabling preventive and prognostic measures for breast cancer models. These empirical findings underscore the potential of advanced machine-learning-driven neuro-architecture for next-generation predictive-oncology solutions that may facilitate treatment strategies. © 2025 Elsevier B.V.

关键词： Risk assessment

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Numerical treatment of fractional order Buruli ulcer and cholera model by using neural network approach

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Knowledge-Based Systems 2025年 320卷

作者： Sabir, Zulqurnain Abdelkawy, M.A. Raja, Muhammad Asif Zahoor Ali, M․ R. Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Riyadh11989 Saudi Arabia Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Basic Engineering Science Department Benha Faculty of Engineering Benha University Benha National University Egypt

Purpose: The purpose of these investigations is to design a neuro computing solver based on the Levenberg-Marquardt backpropagation neural network for the fractional order confection Buruli ulcer and cholera model, which is divided into ten different categories. Method: The numerical solutions of the Buruli ulcer and cholera model are obtained through the reliable stochastic approach. A dataset based Adam scheme is designed that implemented to decrease the mean square error by splitting the data 12%, 12% for both endorsement and testing, while 76% is applied for training. Three cases based on the fractional order values 0.5, 0.7 and 0.9 are used to present the numerical performances of the model. The structure of neural network contains twelve neurons, single layer, and log-sigmoid transfer function for the Buruli ulcer and cholera model. Results: The precision of the proposed scheme is checked using the comparison of the outputs, best validation performances around 10–06 to 10–07, and small absolute error as 10–04 to 10–06. Moreover, the some test performances based on taking different proportional indices are programmatic to validate the dependability of the solver. Novelty: The proposed Levenberg-Marquardt backpropagation neural network approach together with twelve neurons, single layer, and log-sigmoid transfer function is applied first time for the fractional order Buruli ulcer and cholera model. © 2025 Elsevier B.V.

关键词： Mean square error

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Enhancing thyroid nodule assessment with deep learning and ultrasound imaging

e-Prime - Advances in Electrical Engineering, Electronics an...

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e-Prime - Advances in Electrical engineering, Electronics and Energy 2025年 11卷

作者： Kumar, Jatinder Panda, Surya Narayan Dayal, Devi Sharma, Manish Computer Section F Block Nehru Hospital Sector-12 PGIMER Chandigarh India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Endocrinology and Diabetes Unit Department of Paediatrics PGIMER Chandigarh India

The thyroid is a tiny, butterfly-shaped gland in the neck which produces hormones that are essential for controlling the body's various metabolic processes. Thyroid nodules, which are abnormal growths or lumps in the thyroid gland, are common thyroid illnesses, as are hypothyroidism, hyperthyroidism, and both. Thyroid issues are most commonly identified and categorised using thyroid ultrasonography (USG) images. They can have a range of effects on the body's metabolism and overall health. Developments in artificial intelligence (AI), particularly deep learning (DL), are helping to identify and measure patterns in clinical images because of DL's capacity towards pull out hierarchical attribute representations from images without the need for annotated images. Minimizing unnecessary fine needle aspiration (FNA) requires the essential identification of as many malignant thyroid nodules as possible, distinguishing them from benign ones. This research work introduces a technique for thyroid nodule identification in USGs, employing DL to extract relevant features. Three pre-trained DL models, namely ResNet-18, VGG-19 and AlexNet were fine-tuned before using for classification of thyroid USG images. The models' testing and training were done with Digital Database of Thyroid Ultrasound Images (DDTI) which is gold standard dataset. The results demonstrate a classification accuracy of 97.13%, 90.31% and 83.59% with ResNet-18, VGG-19 and AlexNet, respectively. The experimental findings affirm that the pre-trained network model ResNet-18 achieves superior classification performance compared to VGG-19 and AlexNet. © 2025 The Author(s)

关键词： AlexNet Benign Deep learning Malignant ResNet-18 VGG-19

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Unorthodox parallelization for Bayesian quantum state estimation

arXiv

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arXiv 2025年

作者： Nguyen, Hanson H. Law, Kody J.H. Lukens, Joseph M. School of Electrical Computer and Energy Engineering Research Technology Office Arizona State University TempeAZ85287 United States School of Mathematics University of Manchester ManchesterM13 9PL United Kingdom Elmore Family School of Electrical and Computer Engineering Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Quantum Information Science Section Oak Ridge National Laboratory Oak RidgeTN37831 United States

Quantum state tomography (QST) allows for the reconstruction of quantum states through measurements and some inference technique under the assumption of repeated state preparations. Bayesian inference provides a promising platform to achieve both efficient QST and accurate uncertainty quantification, yet is generally plagued by the computational limitations associated with long Markov chains. In this work, we present a novel Bayesian QST approach that leverages modern distributed parallel computer architectures to efficiently sample a D-dimensional Hilbert space. Using a parallelized preconditioned Crank–Nicholson Metropolis-Hastings algorithm, we demonstrate our approach on simulated data and experimental results from IBM Quantum systems up to four qubits, showing significant speedups through parallelization. Although highly unorthodox in pooling independent Markov chains, our method proves remarkably practical, with validation ex post facto via diagnostics like the intrachain autocorrelation time. We conclude by discussing scalability to higher-dimensional systems, offering a path toward efficient and accurate Bayesian characterization of large quantum systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Markov chains

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A Novel Approach to Smart Grid Energy Management Using the Osprey Optimization Algorithm

A Novel Approach to Smart Grid Energy Management Using the O...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Nissisolomon Rudrapogu N. Hemalatha Sanjay B Warkad A. Jayanthi K. Vinoth B Jayaram Department of Electrical and Electronics Engineering Section Lecturer in EEE Nissisolomon Rudrapogu MBTS Government Polytechnic College Guntur Andhra Pradesh India Department of Electrical and Electronics Engineering Bharath Institute of Higher Education and Research Chennai Tamil Nadu India Department of Electrical Engineering P. R. Pote Patil College of Engineering & Management Amravati Maharashtra Department of Physics Jeppiaar Institute of Technology Sriperumbudhur Tamil Nadu India Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Business Systems RMK Engineering College Thiruvallur Tamil Nadu India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Smart Grid Energy Management (SGEM) denotes the function of optimizing energy flow in the grid while enhancing grid efficiency and stability. One major issue is that increased integration of Renewable Energy Sources (RESs) may add a layer of complexity to EM, which may, in turn, reduce the reliability and stability of the grid. Keeping such challenges in mind, this paper proposes the Osprey Optimization Algorithm (OOA) on its part to enhance SGEM through various facets of load balancing and energy distribution. The objective of this method is to minimize the Mean Absolute Percentage Error (MAPE), enhancing the precision of energy predictions and optimizing grid performance. The OOA optimizes energy distribution in micro grids, improving efficiency and reducing power losses. The performance of the suggested OOA method is assessed by contrasting it with other optimization approaches that are currently in use, including Particle Swarm Optimization (PSO), Multi Objective Genetic Algorithm (MOGA), Sparrow Search Algorithm (SSA), and Multi Objective Wind Driven Optimization (MOWDO). The OOA method is implemented on the MATLAB platform. The outcomes show that the proposed OOA method provides superior performance in minimizing MAPE, achieving the lowest MAPE value of 0.28%, indicating its enhanced accuracy in energy demand forecasting and optimization.

关键词： Renewable energy sources Accuracy System performance Power system stability Prediction algorithms Load management Smart grids Reliability Forecasting Optimization

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