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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qing Liu Qian Gao Jun Fan Zhiqiang Zhang 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) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China China Telecom Digital Intelligence Techonology Co Ltd Jinan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

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.

关键词： Measurement Bridges Contrastive learning Signal processing Data processing Vectors Space exploration Resource management Speech processing Mutual information

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A Transfer Learning-Enabled Optimized Extreme Deep Learning Paradigm for Diagnosis of COVID-19

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computers, Materials & Continua 2022年第1期70卷 1381-1399页

作者： Ahmed Reda Sherif Barakat Amira Rezk Department of Information System Faculty of Computer and Information ScienceMansoura UniversityMansouraEgypt

Many respiratory infections around the world have been caused by ***-19 is one of the most serious coronaviruses due to its rapid spread between people and the lowest survival *** is a high need for computer-assisted diagnostics(CAD)in the area of artificial intelligence to help doctors and radiologists identify COVID-19 patients in cloud *** learning(ML)has been used to examine chest X-ray *** this paper,a new transfer learning-based optimized extreme deep learning paradigm is proposed to identify the chest X-ray picture into three classes,a pneumonia patient,a COVID-19 patient,or a normal ***,three different pre-trainedConvolutionalNeuralNetwork(CNN)models(resnet18,resnet25,densenet201)are employed for deep feature ***,each feature vector is passed through the binary Butterfly optimization algorithm(bBOA)to reduce the redundant features and extract the most representative ones,and enhance the performance of the CNN *** selective features are then passed to an improved Extreme learning machine(ELM)using a BOA to classify the chest X-ray *** proposed paradigm achieves a 99.48%accuracy in detecting covid-19 cases.

关键词： Butterfly optimization algorithm(BOA) covid-19 chest X-ray images convolutional neural network(CNN) extreme learning machine(ELM) feature selection

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Mathematical analysis and stability of thermally magnetized ternary hybrid nanofluid over porous moving wedge with suction/injection and entropy generation

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Results in Engineering 2025年 26卷

作者： Zeeshan Khan, Waris Kouki, Marouan Boukhris, Imed Arishi, Ali Garalleh, Hakim AL Safi, Mohammad Sediq Department of Mathematics and Statistics Bacha Khan University Charsadda KP Pakistan Department of Mathematics and Statistics Hazara University KP Mansehra21120 Pakistan Department of Information System Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia Department of Physics Faculty of Science King Khalid University AbhaP.O. Box 960 Saudi Arabia Department of Industrial Engineering King Khalid University Abha61421 Saudi Arabia Department of Mathematical Science College of Engineering University of Business and Technology Jeddah21361 Saudi Arabia Department Computer Science Allamah University Kabul Afghanistan

Present study investigates the ternary hybrid nanofluid to analyze thermally magnetized flow over a moving wedge with wall permeability and convective boundary conditions. Using stability analysis and suction and injection give the novel contributions to the study. Water host fluid containing titania, silver, and graphene nanoparticles is used. A self-similarity approach is applied to convert the Partial Differential Equations into dimensionless Ordinary Differential Equations. Numerical solution has been obtained through bvp4c method. Duality has been observed due to suction and injection. From the stability analysis it is perceived that first solution is stable while the second solution is unstable. The impact of embedded factors like magnetic factor, wedge angle parameter Prandtl and Eckert numbers, Suction parameter, and thermal radiation on flow characteristics, temperature distribution, and the entropy generation are demonstrated. Entropy generation is compared for the convectional fluid, nanofluid, hybrid nanofluid, and THNF. Similarly, entropy generation for the downhill flow (m=−0.8), flat flow (m=0), Wedge flow (m=0.2), and stagnation flow (m=1) has been investigated for both suction (S=1) and injection (S=−1) and it has been observed that entropy generation is higher fir the THNF compared to other flows. Additionally, for confirmation, the present study is compared with published work. © 2025 The Author(s)

关键词： Heat radiation

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Semantic and Emotional Dual Channel for Emotion Recognition in Conversation

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Zhenyu Zhang, Zhibo Cheng, Yuhu Zhang, Tong Wang, Xuesong Jinan250353 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China China University of Mining and Technology Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou Key Laboratory of Artificial Intelligence and Big Data School of Information and Control Engineering Xuzhou221116 China South China University of Technology Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Emotion recognition in conversation (ERC) aims at accurately identifying emotional states expressed in conversational content. Existing ERC methods, although relying on semantic understanding, often encounter challenges when confronted with incomplete or misleading semantic information. In addition, when dealing with the interaction between emotional and semantic information, existing methods are often difficult to effectively distinguish the complex relationship between the two, which affects the accuracy of emotion recognition. To address the problems of semantic misdirection and emotional cross-talk encountered by traditional models when confronted with complex conversational data, we propose a semantic and emotional dual channel (SEDC) strategy for emotion recognition in conversations to process emotional and semantic information independently. Under this strategy, emotion information provides an auxiliary recognition function when the semantics are unclear or lacking, enhancing the accuracy of the model. Our model consists of two modules: the emotion processing module accurately captures the emotional features of each utterance through contrastive learning, and then constructs a dialogue emotion propagation map to simulate the emotional information conveyed in the dialogue;the semantic processing module combines an external knowledge base to enhance the semantic expression of the dialogue through knowledge enhancement strategies. This divide-and-conquer approach allows us to more deeply analyze the emotional and semantic dimensions of complex dialogues. Experimental results on the IEMOCAP, EmoryNLP, MELD, and DailyDialog datasets show that our approach significantly outperforms existing techniques and effectively improves the accuracy of dialogue emotion recognition. Our code is available at https://***/r/SEDC-FCF1. © 2010-2012 IEEE.

关键词： Contrastive Learning

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Fence Theorem: Towards Dual-Objective Semantic-Structure Isolation in Preprocessing Phase for 3D Anomaly Detection

arXiv

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arXiv 2025年

作者： Liang, Hanzhe Zhou, Jie Chen, Xuanxin Dai, Tao Wang, Jinbao Gao, Can College of Computer Science and Software Engineering Shenzhen University China Shenzhen Audencia Financial Technology Institute Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Faculty of Education Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

3D anomaly detection (AD) is prominent but difficult due to lacking a unified theoretical foundation for preprocessing design. We establish the Fence Theorem, formalizing preprocessing as a dual-objective semantic isolator: (1) mitigating cross-semantic interference to the greatest extent feasible and (2) confining anomaly judgements to aligned semantic spaces wherever viable, thereby establishing intra-semantic comparability. Any preprocessing approach achieves this goal through a two-stage process of Semantic-Division and Spatial-Constraints stage. Through systematic deconstruction, we theoretically and experimentally subsume existing preprocessing methods under this theorem via tripartite evidence: qualitative analyses, quantitative studies, and mathematical proofs. Guided by the Fence Theorem, we implement Patch3D, consisting of Patch-Cutting and Patch-Matching modules, to segment semantic spaces and consolidate similar ones while independently modeling normal features within each space. Experiments on Anomaly-ShapeNet and Real3D-AD with different settings demonstrate that progressively finer-grained semantic alignment in preprocessing directly enhances point-level AD accuracy, providing inverse validation of the theorem’s causal logic. Copyright © 2025, The Authors. All rights reserved.

关键词： Fences

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Optimized spam classification approach with Negative selection algorithm

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Journal of Theoretical and Applied information Technology 2012年第1期39卷 22-31页

作者： Idris, Ismaila Selamat, Ali Faculty of Computer Science and Information System Unuversiti Teknologi Malaysia Malaysia

This paper initializes a two element concentration vector as a feature vector for classification and spam detection. Negative selection algorithm proposed by the immune system in solving problems in spam detection is used to distinguish spam from non-spam (self from non-self). Self concentration and non-self concentration are generated to form two element concentration vectors. In this approach to e-mail classification, the e-mail are considered as an optimization problem using genetic algorithm to minimize the cost function that was generated and then classification of these cost function shall aid in creating a classifier. This classifier will aid in the new formation of algorithm that comprises of both greater efficiency detector rate and also speedy detection of spam e-mail. The algorithm implementation of the research work shall come in stages were spam and non-spam are detected in all phases for an efficient classifier. © 2005 - 2012 JATIT & LLS. All rights reserved.

关键词： Neural networks

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Quality-biased retrieval in online forums

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Journal of Theoretical and Applied information Technology 2012年第1期38卷 55-62页

作者： Albaham, Ameer Tawfik Salim, Naomie Faculty of Computer Science and Information System UTM Johor 81310 Malaysia

This paper presents an ongoing research in utilizing content quality to enhance thread and post retrieval tasks in support oriented forums. First, we adapt the established information quality approach as a theoretical way to understand quality. Then, we outline research areas that help in quantifying quality. Lastly, we propose to use learning to rank method to weight quality parameters while ranking. © 2005 - 2012 JATIT & LLS. All rights reserved.

关键词： Social networking (online)

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The learning performance of support vector machine classification based on Markov sampling

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science China(information sciences) 2013年第3期56卷 107-122页

作者： ZOU Bin PENG ZhiMing XU ZongBen Faculty of Mathematics and Computer Science Hubei University Institute for Information and System Science Xi'an Jiaotong University

The previously known frameworks describing the consistency of support vector machine classification(SVMC) algorithm are usually based on the assumption of independent and identically distributed(i.i.d.) *** this paper we go far beyond these classical frameworks by studying the consistency of SVMC algorithm with uniformly ergodic Markov chain samples based on linear prediction *** establish the bound on the consistency of SVMC algorithm with uniformly ergodic Markov chain samples,and show that SVMC algorithm with uniformly ergodic Markov chain samples is *** by the idea from Markov chain Monto Carlo(MCMC) methods,we introduce a new Markov sampling algorithm for classification to generate uniformly ergodic Markov chain samples from large data set,and present numerical studies on simulated data and benchmark repository using SVMC algorithm.

关键词： SVMC uniformly ergodic Markov chain learning performance Markov sampling linear prediction models

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Development and modeling of advanced systems Na2SnBr6-based perovskite solar cells: a comprehensive study on electron transport layers

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Optics Express 2025年第10期33卷 21152-21172页

作者： Dipta Roy Md Selim Reza Avijit Ghosh Anup Kumar Roy Bivash Ranjan Chowdhury Md Aktarujjaman H A Alrafai Abeer A Hassan Department of Electrical and Computer Engineering California State University at Northridge Northridge CA 91330 USA Department of Electrical and Electronic Engineering Begum Rokeya University Rangpur-5400 Bangladesh Phillip M. Drayer Department of Electrical and Computer Engineering Lamar University Beaumont TX 77710 USA Department of Management Information System International American University Los Angeles CA 90010 USA Department of Information Technology Management Webster University St. Louis MO 63119 USA Department of Chemistry Faculty of Science King Khalid University PO Box 9004 Abha 61413 Saudi Arabia

This study simulates a n-i-p planar heterojunction for Na 2 SnBr 6 -based perovskite solar cells (PSCs) , with Na 2 SnBr 6 as the absorber layer, fluorine-doped tin oxide (FTO) as the substrate, and In 2 S 3 , IGZO, and SnS 2 as electron transport layers (ETLs). Gold (Au) is used as the back contact. Na 2 SnBr 6 was chosen for its low cost, non-toxicity, tunable band gap, excellent electrical properties, and stability. The SCAPS-1D simulator was employed to model the solar cell performance under AM 1.5 G light irradiation, focusing on factors like doping concentration, layer thickness, defect density, and interface defects across three device configurations: Device I (Au/Na 2 SnBr 6 / In 2 S 3 /FTO/Al), Device II (Au/ Na 2 SnBr 6 /IGZO/FTO/Al), and Device III (Au/ Na 2 SnBr 6 /SnS 2 /FTO/Al). Device III achieved the highest PCE of 31.35%, FF of 85.95%, V OC of 0.7926 V, and J SC of 46.01 mA/cm 2 2 , making it a promising candidate for high-efficiency, lead-free perovskite solar cells. Devices I and II showed lower efficiencies of 27.47% and 30.98%, respectively. The study also analyzed quantum efficiency (QE), carrier dynamics, and recombination rates, highlighting the potential of Device III in advancing Na 2 SnBr 6 -based hybrid perovskite solar technologies for future solar energy applications.

关键词： Material properties Solar cell efficiency Solar cells Solar energy Thin film solar cells Transparent conducting oxides

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Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

arXiv

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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

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