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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Qing Gao, Qian Fan, Jun Zhang, Zhiqiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd. No.1999 Shunhua road Shandong Jinan250101 China

ISBN: (纸本)9798350368741

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN-a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model's effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets. © 2025 IEEE.

关键词： graph contrastive learning graph neural networks recommender systems self-supervised learning

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A Comparative Study of Optimized-LSTM Models Using Tree-Structured Parzen Estimator for Traffic Flow Forecasting in Intelligent Transportation

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computers, Materials & Continua 2025年第5期83卷 3369-3388页

作者： Hamza Murad Khan Anwar Khan Santos Gracia Villar Luis Alonso DzulLopez Abdulaziz Almaleh Abdullah M.Al-Qahtani Department of Electronics University of PeshawarPeshawar25120KPKPakistan Department of Project Management(L.A.D.L) Higher Polytechnic School(S.G.V)Universidad Europea del AtlanticoSantander39011Spain Faculty of Engineering Universidad Internacional IberoamericanaCampeche24560Mexico Faculty of Engineering Universidade Internacional do CuanzaCuitoEN250BieAngola Department of Project Management Universidad Internacional IberoamericanaCampeche24560Mexico Department of Project Management Department of Management SciencesUniversid de La RomanaLa Romana22000Dominican Reublic Information Systems Department College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia Department of Electrical and Electronic Engineering College of Engineering&Computer ScienceJazan UniversityJazan45142Saudi Arabia

Traffic forecasting with high precision aids Intelligent Transport systems(ITS)in formulating and optimizing traffic management *** algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational *** address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning *** Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad *** ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of *** also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction *** proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the *** traffic data is then input to the LSTM model with tuned parameters to perform the traffic *** three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM *** results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.

关键词： Short-term traffic prediction sequential time series prediction TPE tree-structured parzen estimator LSTM hyperparameter tuning hybrid prediction model

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Detecting Hallucinations in Large Language Models Using Machine Learning Classifiers: A Comparative Study

Detecting Hallucinations in Large Language Models Using Mach...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Hazem Al-Najjar Nadia Al-Rousan Douaa Al-Rousan Department of Computer Science College of Information Technology University of Petra Amman Jordan Department of computer engineering College of Engineering AIBalqa Applied University Salt Jordan Department of Computer Engineering Faculty of Electrical Engineering and Information Systems German Jordanian University Amman Jordan School of Civil Engineering Universiti Sains Malaysia Nibong Tebal Pulau Pinang Malaysia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This study is concerned with the comprehensive evaluation of machine learning models intended for the detection of hallucination in Large Language Model output. The preprocessing of the prompt and answers using TF-IDF vectorization has been done as a preliminary step before developing a hallucination prediction model. Afterwards, the generated dataset was then divided into training and testing, with percentages equal to 70% and 30%, respectively. The selected machine learning models were trained with the dataset created. In this research, eight various classifiers are implemented and compared. To evaluate the results of our approach, multiple metrics were taken into consideration: accuracy, precision, recall, and Fl-score. While the training results showed exceptional performance with accuracies exceeding 99% in several models, the test results indicated huge challenges in generalization. Random Forest had the highest testing accuracy of 94.77% at 65% precision but with a very low recall of 4.85%. Besides, cross validation is used with random forest and decision tree, the results showed that the cross-validation is better than the test and train splitting model. The study shows the significant trade-offs between precision and recall in hallucination detection, which is especially problematic while working with imbalanced datasets.

关键词： Training Measurement Accuracy Large language models Computational modeling Predictive models Decision trees Random forests Computational intelligence Testing

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On the -differential spectra of two classes of power functions over finite fields

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Applicable Algebra in Engineering, Communication and Computing 2025年 1-30页

作者： Liu, Qian Huang, Zhiwei Chen, Zhixiong Jiang, Rong Zhang, Liupiao Key Laboratory of Information Security of Network Systems College of Computer and Data Science Fuzhou University Fuzhou China Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and Statistics Hubei University Wuhan China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics Kunming China Fujian Key Laboratory of Financial Information Processing Putian University Putian China School of Computer Science and Technology Dongguan University of Technology Dongguan China

Recently, a new concept called multiplicative differential was introduced by Ellingsen et al. [7]. As an extension of the differential uniformity, it is theoretically appealing to determine the properties of c-differential uniformity and the corresponding c-differential spectrum. In this paper, based on certain quadratic character sums and two special elliptic curves over $$\mathbb {F}_p$$ , the $$(-1)$$ -differential spectra of the following two classes of power functions over $$\mathbb {F}_{p^n}$$ is completely determined: (1) $$f_1(x)=x^{\frac{p^n+3}{2}}$$ , where $$p>3$$ and $$p\equiv 3\pmod 4$$ ; (2) $$f_2(x)=x^{p^n-3}$$ , where $$p>3$$ . The obtained result shows that the $$(-1)$$ -differential spectra of $$f_1(x)$$ and $$f_2(x)$$ can be expressed explicitly in terms of n. Moreover, an upper bound of the c-differential uniformity of $$f_2(x)$$ is given.

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A Hybrid Adaptive Filter for Head Tracking in Augmented Reality (AR)-based Flight Simulators

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IEEE Transactions on computers 2025年

作者： Nwobodo, Onyeka J. Kuaban, Godlove Suila Nkemeni, Valery Wereszczynski, Kamil Cyran, Krzysztof A. Silesian University of Technology Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Polish Academy of Sciences Institute of Theoretical and Applied Informatics Poland University of Buea Laboratory of Electrical Engineering and Computing Faculty of Engineering and Technology Buea Cameroon

This paper presents a novel approach for head tracking in augmented reality (AR) flight simulators using an adaptive fusion of Kalman and particle filters. This fusion dynamically balances the strengths of both algorithms, leveraging Kalman filters for computational efficiency and particle filters for handling non-linearities based on real-time factors such as sensor noise and head movement patterns. Our method demonstrates superior tracking precision and reduced latency, making it particularly effective for immersive pilot training in AR-based flight simulations. While focused on flight simulation, our approach holds high potential for broader applications in other AR and virtual reality (VR) environments where precise, real-time head tracking is crucial. These results provide actionable design guidance for developers optimizing tracking systems in environments that require fast response times and high accuracy. Future extensions of this research could explore the generalization of our approach across diverse AR applications, including human-computer interaction (HCI), medical simulations, and gaming. © 1968-2012 IEEE.

关键词： Flight simulators

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Retraction Note: Role of low-concentrated Cu2O3 additives in changing the structural internal phase, optical property, and radiation gamma-ray shielding ability of Ca/Na lead Arseborate glass

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Optical and Quantum Electronics 2025年第4期57卷 1-2页

作者： Gomaa, Hosam M. Saudi, H. A. Yahia, I. S. Zahran, H. Y. Pharaohs-Higher Institute for Computer Information Systems and Management Giza Egypt Department of Physics Faculty of Science Al-Azhar University Nasr City Cairo Egypt Laboratory of Nano-Smart Materials for Science and Technology (LNSMST) Department of Physics Faculty of Science King Khalid University Abha Saudi Arabia

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Optimal Cluster and Node Balancing Technique Using K-Mean Based on Q-Learning for Wireless Sensor Network 15th

Optimal Cluster and Node Balancing Technique Using K-Mean Ba...

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15th International Conference on Intelligent Computing and Networking, IC-ICN 2024

作者： Raparthi, Mohan Dodda, Sarath Babu Maruthi, Srihari Keshta, Ismail Agarwal, Abhishek Software Engineer Alphabet Life Science DallasTX75063 United States Software Engineer Central Michigan University Dallas Texas75009 United States Senior Technical Solutions Engineer University Of New Haven West Haven United States Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Vermont State University Vermont United States

ISBN: (纸本)9789819786305

Several real-time applications can benefit from wireless sensor networks (WSNs) because of their size, cost-effectiveness, and ease of distribution. It is possible for WSNs to change dynamically as a result of external or internal factors, requiring depreciating dispensable redesign. WSNs are traditionally explicitly programmed, making them hard to react dynamically. Q-learning based on K-Means can be used to avoid such scenarios. Q-learning is used to build a learning mechanism from past simulation data. In order to achieve minimal transmission delay, the work is estimated based on throughput, delay, network lifetime, and packet delivery ratio (PDR). The Q-learning-based mechanism is also investigated as an outcome of the simulation and rounds. Throughput, delay, network lifetime, and PDR analysis of the proposed mechanism are all validated by the comparative investigation against three existing models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

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Unmanned aerial vehicle based multi-person detection via deep neural network models

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Frontiers in Neurorobotics 2025年 19卷 1582995页

作者： Alshehri, Mohammed Zahoor, Laiba AlQahtani, Yahya Alshahrani, Abdulmonem AlHammadi, Dina Abdulaziz Jalal, Ahmad Liu, Hui Department of Computer Science King Khalid University Abha Saudi Arabia Faculty of Computer Science Air University Islamabad Pakistan Department of Informatics and Computer Systems King Khalid University Abha Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia Department of Computer Science and Engineering College of Informatics Korea University Seoul Korea Republic of Cognitive Systems Lab University of Bremen Bremen Germany Guodian Nanjing Automation Company Ltd. Nanjing China Jiangsu Key Laboratory of Intelligent Medical Image Computing School of Future Technology Nanjing University of Information Science and Technology Nanjing China

Introduction: Understanding human actions in complex environments is crucial for advancing applications in areas such as surveillance, robotics, and autonomous systems. Identifying actions from UAV-recorded videos becomes more challenging as the task presents unique challenges, including motion blur, dynamic background, lighting variations, and varying viewpoints. The presented work develops a deep learning system that recognizes multi-person behaviors from data gathered by UAVs. The proposed system provides higher recognition accuracy while maintaining robustness along with dynamic environmental adaptability through the integration of different features and neural network models. The study supports the wider development of neural network systems utilized in complicated contexts while creating intelligent UAV applications utilizing neural networks. Method: The proposed study uses deep learning and feature extraction approaches to create a novel method to recognize various actions in UAV-recorded video. The proposed model improves identification capacities and system robustness by addressing motion dynamic problems and intricate environmental constraints, encouraging advancements in UAV-based neural network systems. Results: We proposed a deep learning-based framework with feature extraction approaches that may effectively increase the accuracy and robustness of multi-person action recognition in the challenging scenarios. Compared to the existing approaches, our system achieved 91.50% on MOD20 dataset and 89.71% on Okutama-Action. These results do, in fact, show how useful neural network-based methods are for managing the limitations of UAV-based application. Discussion: Results how that the proposed framework is indeed effective at multi-person action recognition under difficult UAV conditions. Copyright © 2025 Alshehri, Zahoor, AlQahtani, Alshahrani, AlHammadi, Jalal and Liu.

关键词： Drones

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Bee Colony-Reptile Search Optimization Technique for Blood Cell Cancer Detection

Bee Colony-Reptile Search Optimization Technique for Blood C...

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International Conference on computer and Automation Engineering, ICCAE

作者： Aziza I. Hussein Mohammed Ali Saleh Rabab Hamed M. Aly Energy and Technology Research Center College of Engineering Effat University Jeddah Saudi Arabia Computer and Systems Engineering Department Faculty of Engineering Helwan University Egypt Computer Science Department Faculty of Computer Science Nahda University Beni Suef Egypt The Higher Institute for Management and Information Technology Minya Egypt

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

The biosystem is a crucial system grounded in classification and detection, utilizing Artificial Intelligence (AI) approaches or metaheuristic techniques. Currently, cancer of the blood cells is among the deadliest cancers in the world. Acute lymphoblastic leukemia (ALL) is a cancer of blood cells that causes excessive proliferation of lymphocytes. It is extremely time-consuming and expensive to conduct diagnostic calculations. The number of platelets in a patient's blood is computed by a platelet count. A lacking number of platelets can indicate cancer, infection, or other health problems. A patient with too many platelets is at risk for blood strokes. A single drop of blood includes tens of thousands of platelets. The main goal of this paper is how to detect the features of blood cells and classify with predicting cancer type based on platelets analysis by using Bee Colony followed by Reptile Search Optimization (BCRSO) technique. According to the results, BCRSO algorithm performed better in terms of classification efficacy and accuracy rate than other algorithms. Based on simulation results, the proposed method is more effective than previously published research for classification optimization.

关键词： Accuracy Blood platelets Simulation Metaheuristics Cells (biology) Prediction algorithms Cancer detection Classification algorithms Artificial intelligence Cancer

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Charging Load Prediction Method for Electric Vehicles using CNN-GRU-Attention model with SSA optimization strategy

Charging Load Prediction Method for Electric Vehicles using ...

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International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Xinyu Fan Galaktion V. Shvedov Chuqiao Cai Electrical Power Systems Department National Research University “MPEI” Moscow Russian Federation Information Processing and Management Systems Department Faculty of Computer Science and Control Systems Bauman Moscow State Technical University Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

This paper proposes a CNN-GRU-Attention model for short-term charging load prediction at electric vehicle (EV) charging stations. The model leverages the strengths of Convolutional Neural Networks (CNN) for feature extraction and dimensionality reduction, while Gated Recurrent Units (GRU) enhance temporal sequence learning. The attention mechanism assigns dynamic weights to the extracted features, improving the prediction accuracy. Hyperparameters are optimized using the Sparrow Search Algorithm (SSA), and an improved version, ISSA, further enhances model performance. Experimental results demonstrate that the CNN-GRU-Attention model outperforms baseline models, including CNN, GRU, LSTM (Long Short-Term Memory), and BiLSTM (Bidirectional Long Short-Term Memory), with higher prediction accuracy and fewer trainable parameters. The model's predictive capabilities enable optimization of power supply strategies in smart grids. However, it faces challenges in long-term load prediction and computational resource demands during hyperparameter optimization.

关键词： Adaptation models Accuracy Computational modeling Predictive models Hyperparameter optimization Feature extraction Smart grids Convolutional neural networks Optimization Load modeling

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