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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Zhu, Luyao Zhang, Fangfang Feng, Mengyuan Chen, Ke Zhu, Xiaodong Zhang, Mengjie School of Electrical and Information Engineering Zhengzhou University Zhengzhou China Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

ISBN: (纸本)9798350308365

Dynamic flexible job shop scheduling (DFJSS) is an important combinatorial optimisation problem that aims to optimise machine resources to improve production efficiency. Multi-tree genetic programming (MTGP) has been widely used to learn the routing rule and the sequencing rule for DFJSS simultaneously. Unlike traditional genetic programming that only operates crossover on a single tree, MTGP has various cases to conduct crossover since a genetic programming individual consists of more than one tree. Different crossover operators may affect the performance of MTGP for DFJSS. However, the investigation into different crossover operators in MTGP for DFJSS is rare. Specifically, it is not clear what influence will have on MTGP if involving both the routing rule and the sequencing rule for crossover. To this end, this paper provides a comprehensive investigation of four possible crossover cases between multiple scheduling heuristics with MTGP for DFJSS. The four operators are designed according to the number of trees/rules that crossover operator works on, and whether swapping full trees between parents. The results show that although the compared algorithms have comparable results in most scenarios, MTGP with both rules for crossover and the swapping strategy is ranked as the best one. Further analyses show that the sizes of learned rules are highly related to the crossover operators, and crossover involving more rules can increase the rule sizes, and vice versa. In addition, the population diversity and the number of unique features in the learned rules of MTGP with both rules for crossover are increased to learn effective rules. © 2024 IEEE.

关键词： Genetic programming

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Multi-Label MambaOut for Quality Assessment of Low-Field Pediatric Brain MR Images 1st

Multi-Label MambaOut for Quality Assessment of Low-Field P...

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1st MICCAI Challenge on Low Field Pediatric Brain Magnetic Resonance Image Segmentation and Quality Assurance, LISA 2024, held in Conjunction with Medical Image Computing and computer Assisted Intervention Conference, MICCAI 2024

作者： Zhu, Yueyue Jiang, Haotian Cai, Rongqing Chen, Geng National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China

ISBN: (纸本)9783031830105

Magnetic Resonance Imaging (MRI) can be utilized to study the structure of pediatric brains non-invasively. In practice, low-field MRI scanners are widely adopted for pediatric brain imaging. However, the corresponding acquired MRI data usually suffers from severe artifacts, such as noise and motion. Therefore, an effective Quality Assessment (QA) method is essential. To this end, we design a Multi-Label MambaOut (MLMambaOut) model for the low-field pediatric brain MRI QA challenge. Specifically, we view this challenge as a multi-label classification task, utilizing four stages of gated convolution neural network blocks and ML-Decoder to finish the classification with class balance loss. Furthermore, we explore the performance of Mamba and some advanced models for this challenge. We performed extensive experiments on the challenge data, which is low-field and corrupted with seven kinds of artifacts. The results show that our MLMambaOut achieves superior classification results compared with other methods. © The Author(s) 2025.

关键词： Magnetic resonance imaging

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Automatic extraction of lightweight and efficient neural network architecture of heavy convolutional architectures to predict microsatellite instability from hematoxylin and eosin histology in gastric cancer

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Neural Computing and Applications 2024年第25期36卷 15295-15321页

作者： Rostami, Habib Ashkpour, Maryam Behzadi-Khormouji, Hamed Mokhtari, Maral Khayati, Armin Keshavarz, Ahmad Talatian Azad, Saeed Tabesh, Yahya Computer Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran Artificial Intelligence and Intelligent Healthcare Lab Artificial Intelligence and Data Mining Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Faculty of New Science and Technologies University of Tehran Tehran Iran Department of Computer Science Imec-IDLab University of Antwerp Sint-Pietersvliet 7 Antwerp2000 Belgium Shahid Faghihi Hospital Shiraz University of Medical Sciences Shiraz Iran Department of Computer Science Engineering and IT School of Electrical and Computer Engineering Shiraz University Shiraz Iran Electrical Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran IoT and Signal Processing Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Department of Mathematical Sciences Sharif University of Technology Tehran Iran

Cancers have emerged as a significant concern due to their impact on public health and society. The examination and interpretation of tissue sections stained with Hematoxylin and Eosin (H&E) play a crucial role in disease assessment, particularly in cases like gastric cancer. Microsatellite instability (MSI) is suggested to contribute to the carcinogenesis of specific gastrointestinal tumors. However, due to the nonspecific morphology observed in H&E-stained tissue sections, MSI determination often requires costly evaluations through various molecular studies and immunohistochemistry methods in specialized molecular pathology laboratories. Despite the high cost, international guidelines recommend MSI testing for gastrointestinal cancers. Thus, there is a pressing need for a new diagnostic modality with lower costs and widespread applicability for MSI detection. This study aims to detect MSI directly from H&E histology slides in gastric cancer, providing a cost-effective alternative. The performance of well-known deep convolutional neural networks (DCNNs) and a proposed architecture are compared. Medical image datasets are typically smaller than benchmark datasets like ImageNet, necessitating the use of off-the-shelf DCNN architectures developed for large datasets through techniques such as transfer learning. Designing an architecture proportional to a custom dataset can be tedious and may not yield desirable results. In this work, we propose an automatic method to extract a lightweight and efficient architecture from a given heavy architecture (e.g., well-known off-the-shelf DCNNs) proportional to a specific dataset. To predict MSI instability, we extracted the MicroNet architecture from the Xception network using the proposed method and compared its performance with other well-known architectures. The models were trained using tiles extracted from whole-slide images, and two evaluation strategies, tile-based and whole-slide image (WSI)-based, were employed and comp

关键词： Network architecture

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DCEL:classifier fusion model for Android malware detection

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Journal of Systems engineering and Electronics 2024年第1期35卷 163-177页

作者： XU Xiaolong JIANG Shuai ZHAO Jinbo WANG Xinheng Jiangsu Key Laboratory of Big Data Security&Intelligent Processing Nanjing University of Posts and TelecommunicationsNanjing 210023China School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210023China School of Computing and Engineering University of West LondonLondon W55RFUK

The rapid growth of mobile applications,the popularity of the Android system and its openness have attracted many hackers and even criminals,who are creating lots of Android ***,the current methods of Android malware detection need a lot of time in the feature engineering ***,these models have the defects of low detection rate,high complexity,and poor practicability,*** analyze the Android malware samples,and the distribution of malware and benign software in application programming interface(API)calls,permissions,and other *** classify the software’s threat levels based on the correlation of ***,we propose deep neural networks and convolutional neural networks with ensemble learning(DCEL),a new classifier fusion model for Android malware ***,DCEL preprocesses the malware data to remove redundant data,and converts the one-dimensional data into a two-dimensional gray ***,the ensemble learning approach is used to combine the deep neural network with the convolutional neural network,and the final classification results are obtained by voting on the prediction of each single *** based on the Drebin and Malgenome datasets show that compared with current state-of-art models,the proposed DCEL has a higher detection rate,higher recall rate,and lower computational cost.

关键词： Android malware detection deep learning ensemble learning model fusion

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Enhancing the blockchain interoperability through federated learning with directed acyclic graph

IET Blockchain

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IET Blockchain 2023年第4期3卷 238-248页

作者： Xia, Feng Kaiye, Li Songze, Wu yan, Xin Faculty of Data Science City University of Macau Avenida Padre Tomás Pereira China School of Computer Science and Communication Engineering Jiangsu Univestity Zhenjiang China

The use of federated learning to achieve blockchain interoperability has become a hot topic in research, because it enables data exchange without revealing any private information. However, the previous work, such as ScaleSFL (Asia-CCS, 2022), that has implemented federated learning for blockchain interoperability, the throughput of the framework still cannot support the practical applications. Therefore, a federated learning framework based on Directed Acyclic Graph (DAG) is proposed which utilizes sharding mechanism to enhance the blockchain interoperability. By constructing a weighted context graph based on data correlation, reasonable sharding of the dataset is achieved, thereby improving the efficiency of blockchain interoperability. The experimental results show that the federated framework reduces global computation in federated learning by 30% compared with other schemes, while increasing blockchain throughput by nearly 40%. © 2023 The Authors. IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Blockchain

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Probabilistic time series forecasting with deep non-linear state space models

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CAAI Transactions on Intelligence Technology 2023年第1期8卷 3-13页

作者： Heming Du Shouguo Du Wen Li School of Computer Science College of Engineering and Computer ScienceAustralian National UniversityCanberraAustralian Capital TerritoryAustralia Shanghai Municipal Big Data Center ShanghaiChina Department of Economic Statistics School of Statistics and InformationShanghai University of International Business and EconomicsShanghaiChina

Probabilistic time series forecasting aims at estimating future probabilistic distributions based on given time series *** is a widespread challenge in various tasks,such as risk management and decision *** investigate temporal patterns in time series data and predict subsequent probabilities,the state space model(SSM)provides a general *** of SSM achieve considerable success in many fields,such as engineering and ***,since underlying processes in real-world scenarios are usually unknown and complicated,actual time series observations are always irregular and ***,it is very difficult to determinate an SSM for classical statistical *** this paper,a general time series forecasting framework,called Deep Nonlinear State Space Model(DNLSSM),is proposed to predict the probabilistic distribution based on estimated underlying unknown processes from historical time series *** fuse deep neural networks and statistical methods to iteratively estimate states and network parameters and thus exploit intricate temporal patterns of time series *** particular,the unscented Kalman filter(UKF)is adopted to calculate marginal likelihoods and update distributions recursively for non-linear *** that,a non-linear Joseph form covariance update is developed to ensure that calculated covariance matrices in UKF updates are symmetric and positive ***,the authors enhance the tolerance of UKF to round-off errors and manage to combine UKF and deep neural *** this manner,the DNLSSM effectively models non-linear correlations between observed time series data and underlying dynamic *** in both synthetic and real-world datasets demonstrate that the DNLSSM consistently improves the accuracy of probability forecasts compared to the baseline methods.

关键词： Artificial Intelligence machine learning time series

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MDNN:Predicting Student Engagement via Gaze Direction and Facial Expression in Collaborative Learning

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computer Modeling in engineering & Sciences 2023年第7期136卷 381-401页

作者： Yi Chen Jin Zhou Qianting Gao Jing Gao Wei Zhang School of Computer Science Central China Normal UniversityWuhan430079China Computer Science School of ScienceRensselaer Polytechnic InstituteTroy12180USA National Engineering Laboratory of Educational Big Data Application Technology Central China Normal UniversityWuhan430079China

Prediction of students’engagement in aCollaborative Learning setting is essential to improve the quality of *** learning is a strategy of learning through groups or *** cooperative learning behavior occurs,each student in the group should participate in teaching *** showed that students who are actively involved in a class gain *** behavior and facial expression are important nonverbal indicators to reveal engagement in collaborative learning *** studies require the wearing of sensor devices or eye tracker devices,which have cost barriers and technical interference for daily teaching *** this paper,student engagement is automatically analyzed based on computer *** tackle the problem of engagement in collaborative learning using a multi-modal deep neural network(MDNN).We combined facial expression and gaze direction as two individual components of MDNN to predict engagement levels in collaborative learning *** multi-modal solution was evaluated in a real collaborative *** results show that the model can accurately predict students’performance in the collaborative learning environment.

关键词： Engagement facial expression deep network gaze

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Fitness Sharing Chaotic Particle Swarm Optimization (FSCPSO): A Metaheuristic Approach for Allocating Dynamic Virtual Machine (VM) in Fog Computing Architecture

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computers, Materials & Continua 2024年第8期80卷 2557-2578页

作者： Prasanna Kumar Kannughatta Ranganna Siddesh Gaddadevara Matt Chin-Ling Chen Ananda Babu Jayachandra Yong-Yuan Deng Department of Computer Science&Engineering Ramaiah Institute of TechnologyBangalore560054India Department of Artificial Intelligence&Data Science Ramaiah Institute of TechnologyBangalore560054India School of Information Engineering Changchun Sci-Tech UniversityChangchun130600China Department of Computer Science and Information Engineering Chaoyang University of TechnologyTaichung41349Taiwan Department of Information Science and Engineering Malnad College of EngineeringHassan573202India

In recent decades,fog computing has played a vital role in executing parallel computational tasks,specifically,scientific workflow *** cloud data centers,fog computing takes more time to run workflow ***,it is essential to develop effective models for Virtual Machine(VM)allocation and task scheduling in fog computing *** task scheduling,VM migration,and allocation,altogether optimize the use of computational resources across different fog *** process ensures that the tasks are executed with minimal energy consumption,which reduces the chances of resource *** this manuscript,the proposed framework comprises two phases:(i)effective task scheduling using a fractional selectivity approach and(ii)VM allocation by proposing an algorithm by the name of Fitness Sharing Chaotic Particle Swarm Optimization(FSCPSO).The proposed FSCPSO algorithm integrates the concepts of chaos theory and fitness sharing that effectively balance both global exploration and local *** balance enables the use of a wide range of solutions that leads to minimal total cost and makespan,in comparison to other traditional optimization *** FSCPSO algorithm’s performance is analyzed using six evaluation measures namely,Load Balancing Level(LBL),Average Resource Utilization(ARU),total cost,makespan,energy consumption,and response *** relation to the conventional optimization algorithms,the FSCPSO algorithm achieves a higher LBL of 39.12%,ARU of 58.15%,a minimal total cost of 1175,and a makespan of 85.87 ms,particularly when evaluated for 50 tasks.

关键词： Fog computing fractional selectivity approach particle swarm optimization algorithm task scheduling virtual machine allocation

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Spatio-Temporal Dynamic Graph Convolutional Probability Sparse Attention Networks For Traffic Flow Forecasting 14

Spatio-Temporal Dynamic Graph Convolutional Probability Spar...

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14th International Conference on Information Technology in Medicine and Education, ITME 2024

作者： Chen, Linlong Chen, Linbiao Wang, Hongyan Li, Zeping School of Big Data & Information Engineering Guiyang Institute of Humanities and Technology Guiyang China Lanzhou University of Technology College of Computer & Communication Lanzhou China Big Data & Intelligent Engineering School Chongqing College of International Business and Economics Chongqing China

ISBN: (纸本)9798350356670

Accurate traffic flow forecasting plays a vital role in the effective management and control of intelligent transportation systems. However, existing forecasting methods face constraints due to insufficient information sharing and a limited understanding of global temporal relationships, impeding a thorough analysis of temporal and spatial features. To address this issue, we propose a novel approach called ST-DGCPSA (Spatio-Temporal Dynamic Graph Probabilistic Sparse Attention Networks) for traffic flow forecasting. This framework serves as a general forecasting solution, effectively extracting the temporal and spatial characteristics of traffic flow. Firstly, a temporal graph convolution block is constructed, treating historical time slots as graph nodes and employing graph convolution to capture flexible global temporal dependencies. Next, a dynamic graph constructor is designed to explore spatial correlations between nodes and dynamic temporal dependencies across different time points, fully extracting dynamic spatio-temporal relationships. Lastly, the spatio-temporal convolution block enhances the model's ability to capture spatial correlations, temporal dependencies, and multi-scale modeling through the probabilistic sparse attention mechanism, leading to improved forecasting accuracy. Experimental results on four real datasets demonstrate the superiority of ST-DGCPSA over existing techniques. © 2024 IEEE.

关键词： data flow graphs

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MedEx: Enhancing Medical Question-Answering with First-Order Logic based Reasoning and Knowledge Injection 31

MedEx: Enhancing Medical Question-Answering with First-Order...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zafar, Aizan Mishra, Kshitij Ekbal, Asif Department of Computer Science and Engineering Indian Institute of Technology Patna India School of AI and Data Science Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798891761964

In medical question-answering, traditional knowledge triples often fail due to superfluous data and their inability to capture complex relationships between symptoms and treatments across diseases. This limits models' ability to provide accurate, contextually relevant responses. To overcome this, we introduce MedEx, which employs First-Order Logic (FOL)-based reasoning to model intricate relationships between diseases and treatments. We construct FOL-based triplets that encode the interplay of symptoms, diseases, and treatments, capturing not only surface-level data but also the logical constraints of the medical domain. MedEx encodes the discourse (questions and context) using a transformer-based unit, enhancing context comprehension. These encodings are processed by a Knowledge Injection Cell that integrates knowledge graph triples via a Graph Attention Network. The Logic Fusion Cell then combines medical-specific logical rule triples (e.g., co-occurrence, causation, diagnosis) with knowledge triples and extracts answers through a feed-forward layer. Our analysis demonstrates MedEx's effectiveness and generalization across medical question-answering tasks. By merging logical reasoning with knowledge, MedEx provides precise medical answers and adapts its logical rules based on training data nuances. © 2025 Association for Computational Linguistics.

关键词： Question answering

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