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2nd Biennial International Symposium on Fluids and Thermal engineering, FLUTE 2023

作者： Mittal, Mandeep Dhanda, Anil Raghav, Tisha Phanden, Rakesh Kumar Department of Mathematics School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Mathematics Amity Institute of Applied Sciences Amity University Uttar Pradesh Noida India Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789819731725

This research study proposes a cooperative and non-cooperative game theory strategy. The study’s objectives are to examine how carbon emissions impact the supply chain, especially in means of quality control and cost restriction, and to evaluate the effectiveness of game theory in addressing the issue. To gather and evaluate both quantitative and qualitative data for the study, a mixed-methods technique is used. The importance of reducing carbon emissions in supply chains is emphasized in the study, as are the benefits of using cooperative and non-cooperative game theory strategies to achieve this goal. The paper improves knowledge on supply chain management by offering an approach for simulating carbon emissions in a network of supply chains. The results of this study benefit supply chain management and assist academics, policymakers, and sustainability practitioners. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Game theory

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Multi-feature Fusion Deep Network for Skin Disease Diagnosis

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Multimedia Tools and Applications 2025年第1期84卷 419-444页

作者： Gairola, Ajay Krishan Kumar, Vidit Sahoo, Ashok Kumar Diwakar, Manoj Singh, Prabhishek Garg, Deepak Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era deemed to be university Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India School of Computer Science and Artificial Intelligence SR University Warangal Telangana Warangal506371 India

The skin acts as an important barrier between the body and the external environment, playing a vital role as an organ. The application of deep learning in the medical field to solve various health problems has generated increasing interest. The objective of this research is to create a new model using a deep network with multi-feature fusion that enables accurate identification of various skin diseases. This paper proposes a fully fused network (FFN), which includes an improved single block (ISB) and an improved fusion block (IFB) to achieve optimal performance. To implement this, a convolutional neural network-based model for multi-class recognition of skin images has been developed, in which ISB is used to segment diseases in skin images. The IFB module is designed to enhance the effectiveness of the fused network. The model is built and evaluated using our proprietary dataset (Skin_disease_v1) along with publicly available datasets, namely ISIC2016, ISIC2017, and HAM10000. The highest accuracy achieved in experiments was 86% for ISB, 90% for IFB, and 92% for FFN using HAM10000 (for ResNet101V2), HAM10000 (for Resnet50 + ResNet101V2), and HAM10000 (for Resnet50 + ResNet101V2). This shows an accuracy improvement of 9.2%, 13.2%, and 15.2% compared to the state-of-the-art ISB, IFB, and FFN approaches, respectively. Our network outperforms existing networks in terms of performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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Malware Evasion Attacks Against IoT and Other Devices: An Empirical Study

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Tsinghua science and technology 2024年第1期29卷 127-142页

作者： Yan Xu Deqiang Li Qianmu Li Shouhuai Xu School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210023China Department of Computer Science University of Colorado Colorado SpringsColorado SpringsCO 80918USA

The Internet of Things(loT)has grown rapidly due to artificial intelligence driven edge *** enabling many new functions,edge computing devices expand the vulnerability surface and have become the target of malware ***,attackers have used advanced techniques to evade defenses by transforming their malware into functionality-preserving *** systematically analyze such evasion attacks and conduct a large-scale empirical study in this paper to evaluate their impact on *** specifically,we focus on two forms of evasion attacks:obfuscation and adversarial *** the best of our knowledge,this paper is the first to investigate and contrast the two families of evasion attacks *** apply 10 obfuscation attacks and 9 adversarial attacks to 2870 malware *** obtained findings are as follows.(1)Commercial Off-The-Shelf(COTS)malware detectors are vulnerable to evasion attacks.(2)Adversarial attacks affect COTS malware detectors slightly more effectively than obfuscated malware examples.(3)Code similarity detection approaches can be affected by obfuscated examples and are barely affected by adversarial attacks.(4)These attacks can preserve the functionality of original malware examples.

关键词： Android malware obfuscation adversarial examples

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Edge-aware Feature Aggregation Network for Polyp Segmentation

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Machine Intelligence Research 2025年第1期22卷 101-116页

作者： Tao Zhou Yizhe Zhang Geng Chen Yi Zhou Ye Wu Deng-Ping Fan PCA Lab Key Laboratory of Intelligent Perception and Systems for High-dimensional Information of Ministry of EducationSchool of Computer Science and EngineeringNanjing University of Science and TechnologyNanjing210094China School of Computer Science and Engineering Northwestern Polytechnical University(NPU)Xi’an710129China School of Computer Science and Engineering Southeast UniversityNanjing211189China Computer Vision Lab ETH ZürichZürich8092Switzerland

Precise polyp segmentation is vital for the early diagnosis and prevention of colorectal cancer(CRC)in clinical ***,due to scale variation and blurry polyp boundaries,it is still a challenging task to achieve satisfactory segmentation performance with different scales and *** this study,we present a novel edge-aware feature aggregation network(EFA-Net)for polyp segmentation,which can fully make use of cross-level and multi-scale features to enhance the performance of polyp ***,we first present an edge-aware guidance module(EGM)to combine the low-level features with the high-level features to learn an edge-enhanced feature,which is incorporated into each decoder unit using a layer-by-layer ***,a scale-aware convolution module(SCM)is proposed to learn scale-aware features by using dilated convolutions with different ratios,in order to effectively deal with scale ***,a cross-level fusion module(CFM)is proposed to effectively integrate the cross-level features,which can exploit the local and global contextual ***,the outputs of CFMs are adaptively weighted by using the learned edge-aware feature,which are then used to produce multiple side-out segmentation *** results on five widely adopted colonoscopy datasets show that our EFA-Net outperforms state-of-the-art polyp segmentation methods in terms of generalization and *** implementation code and segmentation maps will be publicly at https://***/taozh2017/EFANet.

关键词： Colorectal cancer polyp segmentation edge-aware guidance module scale-aware convolution module cross-level fusion module

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Mode Competition of Low Voltage Backward Wave Oscillator near 500 GHz with Parallel Multi-Beam

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Chinese Journal of Electronics 2024年第2期33卷 488-495页

作者： Xiaoyan ZHAO Jincheng HU Haoran ZHANG Sidou GUO Yuming FENG Lin TANG Kaichun ZHANG Diwei LIU THz Center School of Electronic Science and Engineering University of Electronic Science and Technology of China School of Computer Science Southwest Petroleum University

A backward wave oscillator with parallel multiple beams and multi-pin slow-wave structure(SWS)operating at the frequency above 500 GHz is studied. Both the cold-cavity dispersion characteristics and CST Particle Studio simulation results reveal that there are obvious mode competition problems in this kind of terahertz *** that the structure of the multi-pin SWS is similar to that of two-dimensional photonic crystals, we introduce the defects of photonic crystal with the property of filtering into the SWS to suppress high-order ***, a detailed study of the effect of suppressing higher-order modes is carried out in the process of changing location and arrangement pattern of the point defects. The stable, single-mode operation of the terahertz source is realized. The simulation results show that the ratio of the output peak power of the higher-order modes to that of the fundamental mode is less than 1.9%. Also, the source can provide the output peak power of 44.8 m W at the frequency of 502.2 GHz in the case of low beam voltage of 4.7 kV.

关键词： Mode competition Backward wave oscillator Multi-pin slow-wave structure Parallel multiple beams Defects

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A Hybrid Meta-heuristics Algorithm: XGBoost-Based Approach for IDS in IoT

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SN computer science 2024年第5期5卷 537页

作者： Bajpai, Soumya Sharma, Kapil Chaurasia, Brijesh Kumar Department of Computer Science and Engineering Amity University Madhya Pradesh Maharajpura Dang Gwalior India Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India

In recent years, the importance of the Internet of Things (IoT) and its applications has increased. IoT networks are diverse, allowing for various real-time applications and making them indispensable to daily life. Even though these IoT gadgets make everyday tasks more straightforward, these networks are open to several security risks. IoT networks are currently vulnerable to security assaults due to inadequate security measures, making them attractive targets for different attackers. In this study, the Whale optimization method (WOM) is used to identify the ideal parameters for an intrusion detection model, and XGBoost (Extreme Gradient Boosting) is applied to those parameters that WOM selects. The principal component analysis (PCA) initially performs pre-processing on the gathered network data. To improve the overall model’s capacity for detecting intrusions after training, pre-processed data are first loaded into the WOM-Boost algorithm. The empirical results are applied to two datasets, IoTID20 and UNSW-NB15, and it is demonstrated that the IDS model using this method has greater accuracy, up to 99%, when compared to the results obtained by parameter adjustment in the conventional manner. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Extreme gradient boosting (XGBoost) Internet of things (IoT) Intrusion detection system (IDS) Whale optimization method (WOM)

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Developing a Consumer Electronics Robotics With a Large Language Model Based on a Trustworthy AI Framework

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IEEE Transactions on Consumer Electronics 2025年第1期71卷 2027-2038页

作者： Wu, Hsin-Te Wei, Wei Li, Syuan-Hao Chen, Mu-Yen National Taitung University Computer Science and Information Engineering Taitung950309 Taiwan Xi'an University of Technology School of Computer Science and Engineering Xi'an710048 China National Cheng Kung University Department of Engineering Science Tainan70142 Taiwan

As many countries face the challenges of an aging population and declining birth rates, the demand for labor, particularly for assisting the elderly, is increasing. Traditional robots, being standardized products, require engineers to adjust parameters to fit the unique lifestyle of each household, which is time-consuming and inconvenient. However, with the rapid development of artificial intelligence and consumer electronics, there is a growing need for home robots with smarter interfaces to achieve the goal of intelligent living. This paper proposes a home robot based on a trustworthy AI framework, integrated with large language models (LLM). These LLM can perform natural language processing and object recognition, allowing users to control the robot's operations through natural language commands. This innovation further advances consumer electronics. The robot's arm can remember these actions and operate according to instructions. Additionally, the robot arm is equipped with monitoring functions, capable of overseeing the operation of other robots and using cameras to detect errors. This development is significant in the field of consumer electronics. The robot also uses Long Short-Term Memory (LSTM) networks to predict the motion paths of the robotic arm, ensuring smooth and efficient operation. This integration of AI and robotics aims to enhance the adaptability and functionality of home robots, making them more suitable for the diverse needs of modern households, improving the quality of life for the elderly, and driving innovation in the consumer electronics field. © 1975-2011 IEEE.

关键词： Robotic arms

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CBIR: a novel identification approach for college students in need based on consumer behavior psychology theory

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Neural Computing and Applications 2025年第6期37卷 4663-4677页

作者： Liu, Xinze Liu, Shixi Hu, Xiaojing Zhang, Yudong Fang, Xianwen School of Computer and Information Engineering Chuzhou University Chuzhou239000 China School of Computer Science and Engineering Southeast University Nanjing210096 China School of Mathematics and Big Data Anhui University of Science and Technology Huainan232001 China

The accurate identification of students in need is crucial for governments and colleges to allocate resources more effectively and enhance social equity and educational fairness. Existing approaches to identifying students in need rely on manual operations that include manually extracting consumption behavior information, statistical consumption characteristics and principal component analysis. However, this issue may lead to low prediction accuracy and inefficiency in identifying students in need. We design a three-stage framework to accurately identify college students in need from the perspective of consumer behavior psychology. The consumption behavior information is first obtained from the student consumption records using the consumption behavior clustering approach. The consumption behavior matrix is then built by extracting consumption and spatiotemporal information in different periods. Finally, a novel consumption behavior identification ResNeSt (CBIR) model is proposed to identify college students in need accurately. The experimental results on real datasets show that the CBIR model has higher prediction accuracy than the baseline models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Social psychology

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DA-FR-UNet: A Dual Attention-Enhanced Approach for Retinal Vessel Image Segmentation

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第5期52卷 1325-1335页

作者： Li, Yuanyuan Wu, Jiansheng Liu, Yang School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China University of Science and Technology Liaoning Anshan114051 China

Retinal vessel image segmentation is crucial for the early diagnosis and treatment of ophthalmic diseases. However, accurate segmentation remains challenging due to the complexity and diversity of retinal images, as well as the intricate nature of vascular structures. Existing methods often underperform when dealing with fine vessels and complex backgrounds, limiting their effectiveness in clinical applications. This study proposes a novel retinal vessel image segmentation method, Dual Attention FR-UNet (DA-FR-UNet), which incorporates two innovative attention mechanisms: Enhanced Channel Prior Attention (ECPA) and Hard Triplet Attention (HTA). The ECPA module enhances the model's ability to recognize fine blood vessels by integrating global average pooling and global maximum pooling to effectively capture multi-scale features. In contrast, the HTA module improves the model's understanding of complex vascular structures by simultaneously considering attention information across three dimensions: channel, spatial height, and width. Additionally, the study introduces the testtime augmentation (TTA) technique during the experimental phase to enhance the model's generalizability and robustness. To validate the performance of the DA-FR-UNet model in retinal vessel image segmentation tasks, a series of comparative experiments were conducted on the FIVES and RVD datasets. The experimental results demonstrate that the DA-FR-UNet model, combining the dual-attention mechanism and the TTA strategy, outperforms existing state-of-the-art methods across several key evaluation metrics. On the FIVES dataset, DAFR- UNet achieves a sensitivity of 88.42%, an IoU value of 82.27%, and a Dice coefficient of 89.72%. On the RVD dataset, these metrics are 65.46%, 44.53%, and 61.43%, respectively. These findings highlight the model's significant advantages in processing fine vessels and complex backgrounds, improving its sensitivity and robustness in blood vessel image segmentation. © (2025)

关键词： Image segmentation

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Task offloading delay minimization in vehicular edge computing based on vehicle trajectory prediction

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Digital Communications and Networks 2025年第2期11卷 537-546页

作者： Feng Zeng Zheng Zhang Jinsong Wu School of Computer Science and Engineering Central South UniversityChangsha410083China School of Artificial Intelligence Guilin University of Electronic TechnologyGuilin541000China The Department of Computer Science Universidad de ChileSantiagoChile

In task offloading,the movement of vehicles causes the switching of connected RSUs and servers,which may lead to task offloading failure or high service *** this paper,we analyze the impact of vehicle movements on task offloading and reveal that data preparation time for task execution can be minimized via forward-looking ***,a Bi-LSTM-based model is proposed to predict the trajectories of *** service area is divided into several equal-sized *** the actual position of the vehicle and the predicted position by the model belong to the same grid,the prediction is considered correct,thereby reducing the difficulty of vehicle trajectory ***,we propose a scheduling strategy for delay optimization based on the vehicle trajectory *** the inevitable prediction error,we take some edge servers around the predicted area as candidate execution servers and the data required for task execution are backed up to these candidate servers,thereby reducing the impact of prediction deviations on task offloading and converting the modest increase of resource overheads into delay reduction in task *** results show that,compared with other classical schemes,the proposed strategy has lower average task offloading delays.

关键词： Vehicular edge computing Task offloading Vehicle trajectory prediction Delay minimization Bi-LSTM model

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