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Journal of Network Intelligence 2023年第3期8卷 895-912页

作者： Lu, Jiang-Tao Lin, Kai-Biao Chen, Xin Chen, Jin-Po Department of Computer Science and Technology Xiamen University of Technology Xiamen361024 China

Healthcare fraud is a major problem that affects the quality of healthcare ser-vices. Traditional machine learning methods to address healthcare fraud typically focus on only characteristic patient attributes and ignore the underlying behavioral patterns implicit in a patient’s treatment history. To mine behavioral information implied by healthcare fraudsters, we propose a multibehavioral pattern-based graph neural network (MBGNN) to detect healthcare fraud. The proposed MBGNN first mines behavioral attributes in healthcare heterogeneous graphs by multibehavior pattern decomposition. Here, three sampling strategies are employed to capture groups with the same behavioral pat-terns. Then, a two-step attention mechanism is employed to aggregate the compound semantic information of patients with different behavior patterns. Finally, the learned embeddings by combining semantic information with topological information are used to detect patients with abnormal behaviors. Extensive experiments were conducted on two real-world datasets, and the proposed MBGNN exhibited superior performance compared to state-of-the-art baseline methods. In the anomaly detection task, the proposed MBGNN scored high on F1 and precision metrics. We found that the proposed MBGNN outper-formed existing methods in terms of accuracy. The multiple sampling strategies employed in the proposed method reveal the hidden behavior patterns of healthcare fraudsters. The results obtained by the proposed method are effective and explainable;thus, we believe the proposed can be used to detect healthcare fraud in practical applications. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Topology

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3D Road Network Modeling and Road Structure Recognition in Internet of Vehicles

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computer Modeling in Engineering & sciences 2024年第2期138卷 1365-1384页

作者： Dun Cao Jia Ru Jian Qin Amr Tolba Jin Wang Min Zhu College of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China Department of Computer Science Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia College of Information Science and Technology Zhejiang Shuren UniversityHangzhou310015China

Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles,people, transportation infrastructure, and networks, thereby realizing amore intelligent and efficient transportationsystem. The movement of vehicles and the three-dimensional (3D) nature of the road network cause the topologicalstructure of IoV to have the high space and time *** modeling and structure recognition for 3Droads can benefit the description of topological changes for IoV. This paper proposes a 3Dgeneral roadmodel basedon discrete points of roads obtained from GIS. First, the constraints imposed by 3D roads on moving vehicles areanalyzed. Then the effects of road curvature radius (Ra), longitudinal slope (Slo), and length (Len) on speed andacceleration are studied. Finally, a general 3D road network model based on road section features is *** paper also presents intersection and road section recognition methods based on the structural features ofthe 3D road network model and the road features. Real GIS data from a specific region of Beijing is adopted tocreate the simulation scenario, and the simulation results validate the general 3D road network model and therecognitionmethod. Therefore, thiswork makes contributions to the field of intelligent transportation by providinga comprehensive approach tomodeling the 3Droad network and its topological changes in achieving efficient trafficflowand improved road safety.

关键词： Internet of vehicles road networks 3D road model structure recognition GIS

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DeciLS-PBO: an effective local search method for pseudo-Boolean optimization

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Frontiers of computer science 2024年第5期18卷 239-241页

作者： Luyu JIANG Dantong OUYANG Qi ZHANG Liming ZHANG College of Computer Science and Technology Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering(Ministry of Education) Jilin UniversityChangchun 130012China

1 Introduction Local search method is a rising star for solving combinatorial optimization problems in recent years,and the state-of-the-art local search-based incomplete Maximum Satisfiability(MaxSAT)solversshowpromi... 详细信息

关键词： Max optimization local

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Traffic police gesture recognition based on SlowFast network 5

Traffic police gesture recognition based on SlowFast network

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5th International Conference on computer Vision and Data Mining, ICCVDM 2024

作者： Zhu, Xinting Fang, Ming School of Computer Science and Technology Changchun University of Science and Technology 7186 WeiXing Road Jilin Changchun130022 China School of Artificial Intelligence Changchun University of Science and Technology 7186 WeiXing Road Jilin Changchun130022 China

ISBN: (纸本)9781510682863

As the field of self-driving cars accelerates, the accurate interpretation of traffic officers' signals has become a pivotal technological advancement. Current research has explored various feature representation-based methods for traffic police gesture recognition, including RGB frames, optical flow, human skeletons, and point clouds. However, the existing methods still face challenges in spatio-temporal information fusion and feature extraction. Specifically, the skeleton-based method, noted for its focus on actions and compactness, could benefit from improvements in processing speed and robustness. In response to these challenges, the present study proposes a SlowFast network-based approach for traffic police gesture recognition (SF-TPGR). The method uses 3D heatmaps as inputs to convey the relative positional information of joint points through heatmap intensity changes, thereby improving model accuracy and speed by reducing the redundancy of heatmaps for lower limb joints. The SlowFast network architecture is then used for traffic police gesture recognition, effectively modeling rapidly changing actions such as going straight and lane changing with high frame refresh rates. The comparative experiment conducted on the Chinese traffic police gesture dataset shows that the accuracy of this method reaches 98.10%, and the inference time is reduced to 79 ms. Although its accuracy may be slightly lower than some top-performing methods in certain cases, its capability to rapidly extract human movements is a significant advantage. © 2024 SPIE.

关键词： Gesture recognition

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A Method for Enhancing Robustness of Viewpoint Planning Based on Spatial Supervoxels 4

A Method for Enhancing Robustness of Viewpoint Planning Base...

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4th International Conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Chang, Lianjie Qu, Fuheng Liu, Chenghao Wang, Enguo Ding, Shiqi Su, Chengzhi Changchun University of Science and Technology School of Computer Science and Technology Changchun China Harbin Aircraft Industry Group Co. Ltd. Workshop 45 Harbin China Changchun University of Science and Technology School of Mechanical and Electrical Engineering Changchun China Changchun University of Science and Technology School of Artificial Intelligence Changchun China

ISBN: (纸本)9798331531225

In the viewpoint planning task for unknown objects, the accuracy of the perception system and calibration lead to the collected point cloud data containing numerous noise and stratification, which interferes with the viewpoint planning *** address the above problems, this paper proposes a method to enhance the robustness of viewpoint planning based on spatial ***, the spatial voxel environment is constructed to store the triangular meshed point cloud containing noise, which is used to record the positional distribution information of the point cloud in space;then, the voxels containing the point cloud are neighborhood-grown to form a Supervoxel unit, which expands the receptive field;And finally, the point cloud boundaries are detected and categorized using spatial Supervoxel, which are used to guide viewpoint *** verify the effectiveness of the proposed method, this paper develops a robot perception system, uses an aircraft radome as the experimental object and introduces varying levels of system errors for the viewpoint planning *** experimental results indicate that the proposed method can accurately detect and classify point cloud boundaries to guide viewpoint planning, achieving complete surface reconstruction of unknown objects, even in the presence of a certain degree of noise in the perception system. The overall modeling time is approximately 7 minutes, demonstrating strong robustness and real-time performance. © 2024 IEEE.

关键词： Robot programming

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Adaptive Flowing Traffic Prediction in Contention Random Access for Optimizing Virtual/Physical Resource in B5G/5G New Radio and Core Network

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IEEE Transactions on Network science and Engineering 2024年第2期11卷 1934-1946页

作者： Chang, Ben-Jye Lin, Yu-Ting National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu City64002 Taiwan

For differentiating and customizing different classes of traffic and virtualizing physical resources of networks and machines, B5G/5G specifies several novel mechanisms, including VNF, SDN, Service Function Chaining, Network Slicing, MEC, etc. In B5G/5G, the out-of-band contention based random access using PHY subframe preambles is specified before data transmissions. However, B5G/5G requires dynamically efficient approaches for realizing real 5G, including randomly contending limited sharing channel preambles, dynamically determining flow slicing of SFC, dynamically determining PM/VNFi/VNFc resources, while minimizing carrying cost and energy consumption via multi-link/multi-path 5G network. This paper thus proposes the efficient Adaptive Slice Flowing Prediction in Contention Random Access for Optimizing Virtual/Physical Resource in 5G (APROR) consisting of two phases: Dynamic Flow Random Contention of 5G (dFRC) and Adaptive Real-time Predictive Flow Data Rate of diverse types of slicing (aRPF). Numerical results show APROR outperforms all approaches in performance metrics: contention collision probability, access delay, E2E delay, loss, throughput, etc. Consequently, several contributions are definitely achieved: 1) to dynamically determine the preamble configuration modes and dynamic flow backoff, 2) to adaptively differentiate collision domains for different types of flows and minimize collision probability, and 3) to determine the optimal Virtual/Physical Resource achieving optimal SFC corresponding to network slices. © 2013 IEEE.

关键词： Real time systems

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Machine and deep learning techniques for the prediction of diabetics: a review

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Multimedia Tools and Applications 2025年第18期84卷 19425-19549页

作者： Modak, Sandip Kumar Singh Jha, Vijay Kumar Department of Computer Science & Engineering Sarala Birla University Jharkhand Ranchi India Department of Computer Science & Engineering Birla Institute of Technology Mesra Ranchi India

Diabetes has become one of the significant reasons for public sickness and death in worldwide. By 2019, diabetes had affected more than 463 million people worldwide. According to the International Diabetes Federation report, this figure is expected to rise to more than 700 million in 2040, so early screening and diagnosis of diabetes patients have great significance in detecting and treating diabetes on time. Diabetes is a multi factorial metabolic disease, its diagnostic criteria are difficult to cover all the ethology, damage degree, pathogenesis and other factors, so there is a situation for uncertainty and imprecision under various aspects of the medical diagnosis process. With the development of Data mining, researchers find that machine learning and deep learning, playing an important role in diabetes prediction research. This paper is an in-depth study on the application of machine learning and deep learning techniques in the prediction of diabetics. In addition, this paper also discusses the different methodology used in machine and deep learning for prediction of diabetics since last two decades and examines the methods used, to explore their successes and failure. This review would help researchers and practitioners understand the current state-of-the-art methods and identify gaps in the literature. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Data mining

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Modern Standard Arabic speech disorders corpus for digital speech processing applications

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International Journal of Speech technology 2024年第1期27卷 157-170页

作者： Alqudah, Assal A. M. Alshraideh, Mohammad A. M. Abushariah, Mohammad A. M. Sharieh, Ahmad A. S. Department of Computer Science King Abdullah II School of Information Technology The University of Jordan Amman Jordan Department of Computer Information Systems King Abdullah II School of Information Technology The University of Jordan Amman Jordan Department of Computer Science Faculty of Science and Information Technology Al-Zaytoonah University of Jordan Amman Jordan

Digital speech processing applications including automatic speech recognition (ASR), speaker recognition, speech translation, and others, essentially require large volumes of speech data for training and testing purposes. Although there are available speech corpora, speech data for speakers suffering speech disorders are hardly available for many languages including Arabic language. Consequently, developing digital speech processing applications that target the entire society becomes hard due to the unavailability of speech corpora that contain sufficient speakers’ variations including healthy and disordered speech. This research presents our work towards developing a Modern Standard Arabic (MSA) speech corpus for speakers suffering distortion and substitution articulation disorders. The speech corpus was recorded by 40 (20 male and 20 female) Jordanian speakers who suffer either distortion or/and substitution articulation disorders. This speech corpus can be used for various applications including ASR, speech and hearing, and others. Part of this speech corpus is used for developing and evaluating an ASR for MSA using the Carnegie Mellon university (CMU) Pocketsphinx tools based on Mel-Frequency Cepstral Coefficients (MFCC) and Hidden Markov Model (HMM) techniques. Furthermore, Linear Discriminant Analysis (LDA) and Maximum Likelihood Linear Transform (MLLT) optimization techniques were applied. Using three different testing data sets, this work obtained 98.38% and 1.76% average word recognition correctness rate (WRCR) and average Word Error Rate (WER), respectively, for speaker-dependent and text-independent. For speaker-independent and text-dependent, this work obtained 99.37% and 0.68% average WRCR and average WER, respectively, whereas for speaker-independent and text-independent this work obtained 96.53% and 4.00% average WRCR and average WER, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Natur

关键词： Hidden Markov models

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Fuzzy Reliability and Availability of System under a Calendar-based Inspection Involving Multiple Failures and Its Application to Wind Turbine System

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Journal of Systems science and Systems Engineering 2024年第2期33卷 187-206页

作者： Mintu Kumar Himani Pant S.B.Singh Department of Mathematics Statistics and Computer Science G.B.Pant University of AgricultureTechnologyPantnagarUttarakhandIndia

Uncertainty is an important factor that needs to be considered while analyzing the performance of any engineering *** order to quantify uncertainty,fuzzy set theory is frequently used by most of researchers,including energy system *** to the classical reliability theory,component lifetimes have crisp parameters,but due to uncertainty and inaccuracy in data,it is sometimes very difficult to determine the exact values of these parameters in real-world *** overcome this difficulty in the current research,failure and repair rates were taken as triangular fuzzy numbers to determine the fuzzy availability of a system undergoing calendar-based periodic inspection subject to multiple failure modes(FMs).It was assumed that each component in the system had an exponential failure rate and repair rate with fuzzy *** FMs were explicitly taken into account when a functional state of the system was *** FM had a random failure *** the occurrence of any failure,a random time was selected for the relevant corrective repair *** proposed research was studied for one of the major sources of green energy,namely a wind turbine system wherein all the derived propositions have been implemented on it.

关键词： Fuzzy reliability fuzzy availability wind turbine system multiple failure modes triangular fuzzy number

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Explainable cost-sensitive deep neural networks for brain tumor detection from brain MRI images considering data imbalance

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Multimedia Tools and Applications 2025年 1-28页

作者： Shawon, Md Tanvir Rouf Shibli, G. M. Shahariar Ahmed, Farzad Joy, Sajib Kumar Saha Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh

This paper presents a research study on the use of Convolutional Neural Network (CNN), ResNet50, InceptionV3, EfficientNetB0 and NASNetMobile models to efficiently detect brain tumors in order to reduce the time required for manual review of the report and create an automated system for classifying brain tumors. An automated pipeline is proposed, which encompasses five models: CNN, ResNet50, InceptionV3, EfficientNetB0 and NASNetMobile. The performance of the proposed architecture is evaluated on a balanced dataset and found to yield an accuracy of 99.33% for fine-tuned InceptionV3 model. Furthermore, Explainable AI approaches are incorporated to visualize the model’s latent behavior in order to understand its black box behavior. To further optimize the training process, a cost-sensitive neural network approach has been proposed in order to work with imbalanced datasets which has achieved almost 4% more accuracy than the conventional models used in our experiments. The cost-sensitive InceptionV3 (CS-InceptionV3) and CNN (CS-CNN) show a promising accuracy of 92.31% and a recall value of 1.00 respectively on an imbalanced dataset. The proposed models have shown great potential in improving tumor detection accuracy and must be further developed for application in practical solutions. We have provided the datasets and made our implementations publicly available at -https://***/shahariar-shibli/Explainable-Cost-Sensitive-DeepNeural-Networks-for-Brain-Tumor-Detection-from-Brain-MRI-Images. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Deep neural networks

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