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A novel partitioned random forest method based Facial Emotion Recognition

IEEE Access 2025年 13卷 75089-75103页

作者： Heidari, Hanif Murugappan, M. Shaikh-Mohammed, Javeed Chowdhury, Muhammad E. H. Damghan University Department of Applied Mathematics Damghan Iran Research Lab Department of Electronics and Communication Engineering Block 4 Doha13133 Kuwait Vels Institute of Sciences Technology and Advanced Studies Department of Electronics and Communication Engineering Faculty of Engineering Tamilnadu Chennai India Prince Sattam bin Abdulaziz University College of Applied Medical Sciences Department of Biomedical Technology Al-Kharj11942 Saudi Arabia Qatar University Department of Electrical Engineering Doha2713 Qatar

Facial emotion recognition (FER) has been applied to various sectors, including e-learning, marketing, humanoid robot design, HMI/HCI applications, and medicine. The rapid development of intelligent technologies has led researchers to strive to improve facial emotion recognition techniques. A range of machine learning (ML) methods can be used to recognize facial expressions based on data from small to large datasets. Random Forest (RF) is simpler and more efficient than other ML algorithms. Some modified versions of RF have been developed to improve classification accuracy in the literature. Most improved RF versions modify attribute selection processes or combine them with other machine learning algorithms, increasing their complexity. Identifying an appropriate training dataset and determining its size remain open questions. The partitioned random forests (PRFs) approach is proposed as a modified strategy for improving FER. The proposed method divides multiple regions (different data lengths) into the feature space, allowing the algorithm to learn more complex decision boundaries. Using three statistical measures Lyapunov exponents (LE), Correlation Dimension (CD), and approximate entropy (AE), we evaluated the performance of machine learning algorithms over different data lengths. A crucial role for classification accuracy is played by the Lyapunov exponent or LE and the size of the dataset. A PRF is more effective on smaller datasets and with higher LE values. The proposed method for partitioning the datasets has been successfully tested on the FER dataset to classify six basic emotions (sadness, anger, fear, surprise, disgust, and happiness). Based on our numerical results, PRF performed better than traditional RF and other ML methods for FER, providing 98.37% mean absolute accuracy. Thus, a robust and useful method for improving classification rates is proposed for both small and large datasets. © 2013 IEEE.

关键词： Adversarial machine learning

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research on Biomedical engineering 2025年第2期41卷 1-19页

作者： A, Padmashree Sankar, P. Alkhayyat, Ahmad Muniyandy, Elangovan Department of Computer Science and Business Systems Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam India Department of Computer Science and Engineering Saveetha Engineering College Tamil Nadu Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Applied Science Research Center Applied Science Private University Amman Jordan

Purpose: The primary objective of this research is to develop a comprehensive framework for the analysis of brain tumor images, addressing the complexities of detection, segmentation, and classification. Given the intricate nature of brain anatomy and tumor variability, the study aims to enhance the accuracy and efficiency of brain tumor diagnosis using advanced imaging and processing techniques. Methods: The framework utilizes Brain Tumor Segmentation (BraTS) 2019 and 2020 datasets, applying an AMW-PM filter to enhance image quality. Tumor segmentation employs a slimmable transformer with hybrid axial attention for precision. Classification uses similarity navigated graph neural networks (SNGNNs), optimized with the crayfish algorithm (CFO) to minimize loss and enhance performance. Results: The framework’s effectiveness was validated using key performance metrics, showing significant improvements in tumor detection and classification. The combination of advanced segmentation techniques and the SNGNN classifier, optimized by the CFO, led to a fivefold increase in diagnostic accuracy. These improvements demonstrated a substantial enhancement in the model's ability to correctly identify tumor types and provide reliable outputs for clinical use. Conclusion: The proposed framework delivers a robust solution for brain tumor analysis, integrating cutting-edge techniques for image processing, segmentation, and classification. The results not only highlight improvements in diagnostic accuracy but also contribute to better clinical decision-making in managing brain tumors. This framework sets the foundation for future advancements in brain tumor diagnostics. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： Graph neural networks

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Synergizing vision transformer with ensemble of deep learning model for accurate kidney stone detection using CT imaging

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Alexandria engineering Journal 2025年 127卷 357-373页

作者： Alzughaibi, Arwa Alanazi, Adwan A. Alshahrani, Mohammed Hilali Jaghdam, Ines Hassaballa, Abaker A. Applied college Taibah University Madinah Saudi Arabia Department of Computer Science and Information College of Computer Science and Engineering University of Hail Hail Saudi Arabia Department of Computer Sciences Applied College Najran University Najran66462 Saudi Arabia Department of Computer Science and Information Technology Applied College Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia Department of Mathematics Faculty of Science Northern Border University Arar73213 Saudi Arabia

The disease of Kidney stones is a risky disease for individuals all over the world. Many people with kidney stones in the early phase do not detect it as an illness, and it harms the organ gradually. Precise analysis of kidney illness is vital, as it is a major health concern that needs accurate detection for appropriate and effective treatment. CT scans are one of the most extensively accessible imaging models, and they are employed for effective diagnosis. Deep learning (DL) techniques are gradually identified as beneficial tools for analyzing illness in the medical field. However, present techniques employing deep networks often meet low accuracy and overfitting challenges, demanding further alteration for optimum performance. This study presents a Leveraging Flying Foxes Optimization with an Ensemble of Deep Learning for Accurate Kidney Stone Detection (LFFOEDL-AKSD) technique in CT scans. The presented LFFOEDL-AKSD technique mainly focuses on detecting kidney stones using CI imaging. At first, the presented LFFOEDL-AKSD technique applies the pre-processing phase, which involves image resizing for uniform CT scan dimensions and data augmentation through transformations like rotation and flipping to reduce overfitting, sobel filtering (SF) sharpens edges, and the data is separated into training, validation, and testing sets for model development. The presented LFFOEDL-AKSD technique employs the swin transformer (ST) model for the feature extraction method. Furthermore, the majority voting ensemble of three DL approaches, such as the graph convolutional network (GCN), temporal convolutional network (TCN), and three-dimensional convolutional autoencoder (3D-CAE) approaches, are employed to increase the precision and reliability of the kidney stone recognition. Finally, the presented LFFOEDL-AKSD technique implements the flying foxes optimization (FFO) approach for the hyperparameter tuning involved in the ensemble learning models. An extensive experiment is conduct

关键词： Flying foxes optimization Ensemble deep learning Kidney stone detection CI imaging Swin transformer

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The TMAP Framework: Describing the Design Space of Technology-Mediated Audience Participation in Live Music

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IEEE Access 2025年 13卷 38593-38608页

作者： Hödl, Oliver Turchet, Luca University of Vienna Faculty of Computer Science Cooperative Systems Research Group Vienna1090 Austria University of Applied Sciences Upper Austria Department for Smart and Interconnected Living Hagenberg4232 Austria University of Trento Department of Information Engineering and Computer Science Trento38123 Italy

Technology-mediated audience participation (TMAP) offers a wide variety of ways to enhance the involvement of spectators during a music performance. Technological change has created rich new opportunities for such interactive performance experiences. Effective design of TMAP requires balancing knowledge from diverse human and technological perspectives and taking into account very different characteristics and requirements in live music performance. However, research in this distinctive area of interaction design is limited, and the provision of practical guidance for designers has been scarcely addressed thus far. To bridge this gap, we propose the TMAP Framework, which describes the design space of audience participation in a practice-oriented way to support design-related processes. This descriptive framework is grounded on existing literature from the last 25 years, and has been iteratively improved and peer reviewed with different experts. It was validated using actual TMAP performances not included in the framework development. The TMAP Framework contains 180 entities in a tree-like sorted structure on four levels. Alongside the contribution to knowledge of extensively describing the design space of TMAP in live music, with the validation of the framework, we demonstrate its completeness, stability and generalisability. In this regard, the framework may also serve as a common language between different perspectives and practitioners, thus addressing the highly interdisciplinary nature of the TMAP field. © 2013 IEEE.

关键词： Economic and social effects

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Blow up,Growth and Decay of Solutions for Class of a Coupled Nonlinear Viscoelastic Kirchhoff Equations with Variable Exponents and Fractional Boundary Conditions

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Acta Mathematicae Applicatae Sinica 2025年第2期41卷 344-374页

作者： Abdelbaki CHOUCHA Salah BOULAARAS Djamel OUCHENANE RashidJAN Department of Material Sciences Faculty of SciencesAmar Teleji Laghouat UniversityAlgeria Laboratory of Mathematics and Applied Sciences Ghardaia UniversityAlgeria Department of Mathematics College of ScienceQassim UniversityBuraydah51452Saudi Arabia Department of Mathematics Faculty of SciencesAmar Teleji Laghouat UniversityAlgeria Institute of Energy Infrastructure(IEI) Department of Civil EngineeringCollege of EngineeringUniversiti Tenaga Nasional(UNITEN)Putrajaya CampusJalan IKRAM-UNITEN43000 KajangSelangorMalaysia Mathematics Research Center Near East University TRNCMersin 10Nicosia 99138Turkey

We examine a quasilinear system of viscoelastic equations in this study that have fractional boundary conditions,dispersion,source,and *** discovered that the solution of the system is global and constrained under the right assumptions about the relaxation functions and initial *** that,it is demonstrated that the blow-up has negative initial ***,the growth of solutions is demonstrated with positive initial energy,and the general decay result in the absence of the source term is achieved by using an integral inequality due to Komornik.

关键词： viscoelastic equation blow up nonlinear equations exponential growth general decay variable exponents

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Borel exceptional values of q-shift polynomials

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Annali dell'Universita di Ferrara 2025年第1期71卷 1-12页

作者： Tejuswini, M. Shilpa, N. Department of Mathematics and Research Center in Applied Mathematics M E S College of Arts Commerce and Science Affiliated to Bengaluru City University Karnataka Malleswaram Bengaluru 560003 India Department of Mathematics School of Engineering Presidency University Karnataka Itagalpur Bengaluru 560064 India

This paper deals with the extension of Hayman Conjecture to q-shift differential polynomials generated by meromorphic functions. We prove that the differential polynomials assumes every finite value infinitely often when sharing two Borel exceptional Values with appropriate conditions. Few examples are given to prove that the conditions are inevitable. © The Author(s) under exclusive license to Università degli Studi di Ferrara 2024.

关键词： 30D35 Borel exceptional value Meromorphic function q-shift polynomials Zero order

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Modulational instability, bifurcation study, sensitivity analysis, and Jacobi elliptic waves of a resonant nonlinear Schrödinger equation

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Alexandria engineering Journal 2025年 126卷 441-447页

作者： Hosseini, K. Alizadeh, F. Kheybari, S. Sirisubtawee, Sekson Osman, M.S. Department of Mathematics Near East University TRNC Nicosia Turkey Research Center of Applied Mathematics Khazar University Baku Azerbaijan Faculty of Engineering and Natural Sciences Istanbul Okan University Istanbul Turkey Faculty of Art and Science University of Kyrenia TRNC Mersin 10 Kyrenia99320 Turkey Department of Mathematics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok10800 Thailand Centre of Excellence in Mathematics CHE Si Ayutthaya Road Bangkok10400 Thailand Department of Mathematics Faculty of Science Cairo University Giza12613 Egypt

The current paper formally investigates the propagation of specific waves modeled by a resonant nonlinear Schrödinger equation (RNLSE). In particular, in-depth research is conducted on the RNLSE, which involves various effects such as Bohm potential, detuning effect, etc. The study begins with the modulational instability (MI) of the governing model and goes on with its bifurcation analysis (BA) using the dynamical system theory. Additionally, a sensitivity analysis (SA) is performed to ensure that minor changes in seed values do not adversely affect the solution's stability. The paper ends with retrieving several Jacobi elliptic and soliton waves and analyzing the impact of nonlinear parameters on the dynamics of such waves. The outcomes effectively show how to control the width and amplitude of Jacobi elliptic and soliton waves. © 2025 The Authors

关键词： Schrodinger equation

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Revisiting Location Privacy in MEC-Enabled Computation Offloading

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4396-4407页

作者： Li, Jingyi Ou, Wenzhong Ouyang, Bei Ye, Shengyuan Zeng, Liekang Chen, Lin Chen, Xu Sun Yat-sen University School of Computer Science and Engineering Guangzhou510275 China Chinese University of Hong Kong Department of Information Engineering Hong Kong Macao Polytechnic University Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence China

Mobile Edge Computing (MEC) revolutionizes real-time applications by extending cloud capabilities to network edges, enabling efficient computation offloading from mobile devices. In recent years, the location privacy concern within MEC offloading has been recognized, prompting the proposal of various methodologies to mitigate this concern. However, this paper demonstrates that the prevailing privacy protection methods exhibit vulnerabilities. First, we analyze the shortcomings of current methodologies through both system modeling and evaluation metrics. Then, we introduce a Learning-based Trajectory Reconstruction Attack (LTRA) to expose the weaknesses, achieving up to 91.2% reconstruction accuracy against the state-of-the-art protection method. Further, based on w-event differential privacy, we propose an l-trajectory differentially private mechanism, i.e., OffloadingBD. Compared to the existing works, OffloadingBD provides more flexible and enhanced protection with sound privacy theoretical guarantee. Lastly, we conduct extensive experiments to evaluate LTRA and OffloadingBD. The experiment results show that LTRA has good generalization ability and OffloadingBD showcases a superior balance between privacy and utility compared with baselines. © 2005-2012 IEEE.

关键词： Differential privacy

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Real-Time Facial Recognition System for Secure College Bus Transport using Deep Learning Techniques 3

Real-Time Facial Recognition System for Secure College Bus T...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kavitha, M.N. Saranya, S.S. Pragatheeswari, E. Priya, N. Kaviya Ranjith, S.R. Vanchivel, N. Rahul Kongu Engineering College Department of Computer Science and Design Tamil Nadu India Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Tamil Nadu Coimbatore India Kongu Engineering College Department of Computer Science and Engineering Tamil Nadu Erode India Kongu Engineering College Department of Csd Tamil Nadu India

ISBN: (纸本)9798331509675

The proposed system aims to enhance student transportation security through real-time face detection and recognition. Leveraging the MTCNN framework for accurate face detection and the FaceNet model for reliable face recognition, the system ensures only authorized students can board designated buses by comparing captured faces with a pre-registered database. In case of mismatches, alerts are triggered to notify drivers and authorities. Additionally, parallel implementation of Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers improves authentication accuracy. The system's deep learning-based architecture ensures robust performance under varying environmental conditions and supports continuous learning for long-term reliability. © 2025 IEEE.

关键词： Students

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Nanoimprint lithography-assisted block copolymer self-assembly for hyperfine fabrication of magnetic patterns based on L10-FePt nanoparticles

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Science China Chemistry 2025年第5期68卷 2027-2034页

作者： Jie Zhang Yi Chen Guijun Li Zelin Sun Zhengong Meng Wai-Yeung Wong Department of Applied Biology and Chemical Technology and Research Institute for Smart Energy The Hong Kong Polytechnic University The Hong Kong Polytechnic University Shenzhen Research Institute Division of Integrative Systems and Design and Department of Electronic and Computer EngineeringThe Hong Kong University of Science and Technology School of Flexible Electronics (Future Technology) and Institute of Advanced Materials (IAM) Nanjing Tech University

L10-FePt-type bit-patterned media has provided a promising alternative for ultrahigh-density magnetic recording systems in the current digital era, but rapid fabrication of magnetic patterns with hyperfine bit islands is still challenging, especially with the target for miniaturization and scalable production simultaneously. Herein, Fe,Pt-containing block copolymers were utilized as single-source precursors for solution-processable patterning and subsequent generation of the demanding magnetic FePt dots by in situ pyrolysis. High-throughput nanoimprint lithography was initially employed to fabricate the predefined bit cells precisely,and then the intrinsic self-assembly of phase-separated block copolymers further drove the formation of accurate bit *** from the synergistic effect of top-down lithographic approach and bottom-up self-assembly, the customizable patterns could be achieved for large-scale mass production in targeted areas, but high-density isolated dots could also be accurately aligned along the patterned features after subsequent self-assembly. This reliable strategy would provide a good avenue to precisely construct ultrahigh-density magnetic data storage devices.

关键词： organometallic precursor magnetic nanoparticles block copolymer self-assembly nanoimprint lithography

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