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9th IEEE Smart World Congress, SWC 2023

作者： Li, Yin Fu, Anmin Zhang, Yuqing University of Chinese Academy of Sciences National Computer Network Intrusion Protection Center Beijing China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350319804

The ICS protocols undertake critical functions in industrial control systems, by enabling real-time data exchange, data acquisition, parameter configuration, status monitoring, fault diagnosis, command issuance and execution. It serves as the foundation for the collaborative operations of all components in industrial control systems. With the development of internet technology, industrial control systems, which were originally physically isolated, have become new targets for hackers. However, due to the high level of privatization of ICS protocols and their complex workflow, existing methods are insufficient in detecting vulnerabilities in these protocols. This paper proposes a black-box fuzzing test method based on sensing the program runtime feedback. By collecting run-time statistics, including CPU utilization, memory utilization, disk I/O, and message interaction, our method is able to determine whether the program has triggered a new execution path, thus improving the efficiency of the fuzzing procedure. We evaluated our prototype on five open-source ICS protocol programs and discovered six reported vulnerabilities, among which five could cause the industrial control programs to become unresponsive or crash. © 2023 IEEE.

关键词： Fuzzing ICS Protocols

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Drug-Drug Interaction Prediction Based on Probability Transfer Multi-modal Feature Representation Learning

Drug-Drug Interaction Prediction Based on Probability Transf...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wei, Yu Wang, Lei Yu, Chang-Qin Yang, Shuo Wei, Meng-Meng You, Zhu-Hong Guangxi Academy of Science Nanning China Zaozhuang Univerity College of Information Science and Enginnering Shandong China Xijing Univerity School of Information Engineering Xi'an China Xuzhou University of Technology School of Civil Engineering Xuzhou China China University of Mining and Technology School of Computer Science and Technology Xuzhou China Northwestern Polytechnical University School of Computer Science Xi'an China

ISBN: (纸本)9798350386226

In drug discovery and combination therapy, drug-drug interactions can lead to adverse reactions, affecting not only disease treatment but also risking the market withdrawal of new drugs. Traditional experiments in vitro and in vivo are labor-intensive and time-consuming for identifying potential DDIs. Although existing computational methods offer new perspectives for DDIs identification, they still have limitations. This paper innovatively uses the probability transfer matrix combined with Stacked Denoising Autoencoder to propose a model named MultiPT-DDI to calculate the correlation of edge nodes in the adjacency matrix, which effectively learns the multi-level representation of nodes and mitigates the probabilistic bias of the edge nodes in the sparse matrices and the noise of the original data. Specifically, the method first samples multiple bipartite graph networks using random surfing thus obtaining multiple probabilistic transfer matrices. Subsequently, multiple denoising autoencoder modules are employed for layer-wise unsupervised pre-training of the network. Finally, we infer the relationships between drug pairs using the Random Forest algorithm. The experiment obtains the AUC score of 0.9433 and the AUPR score of 0.9372 in the 5-fold cross-validation, significantly outperforming existing models. In the case studies, 26 of the top 30 drug pairs with the highest scores were validated. The empirical evidence indicates that MultiPT-DDI is an effective complementary model for predicting potential DDIs, providing a reliable reference for traditional experimental methods. © 2024 IEEE.

关键词： Transfer matrix method

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A deep learning-based micro-CT image analysis pipeline for nondestructive quantification of the maize kernel internal structure

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植物表型组学（英文） 2025年第1期7卷 225-238页

作者： Juan Wang Si Yang Chuanyu Wang Weiliang Wen Ying Zhang Gui Liu Jingyi Li Xinyu Guo Chunjiang Zhao College of Information Shanghai Ocean UniversityShanghai201306China Information Technology Research Center Beijing Academy of Agriculture and Forestry SciencesBeijing100097China Beijing Key Lab of Digital Plants National Engineering Research Center for Information Technology in AgricultureBeijing100097China Information Technology Research Center Beijing Academy of Agriculture and Forestry SciencesBeijing100097China Beijing Key Lab of Digital Plants National Engineering Research Center for Information Technology in AgricultureBeijing100097China College of Information Shanghai Ocean UniversityShanghai201306China College of Computer Science and Engineering Southwest Minzu UniversityChengduSichuan610225China College of Information Shanghai Ocean UniversityShanghai201306China Information Technology Research Center Beijing Academy of Agriculture and Forestry SciencesBeijing100097China

Identifying and segmenting the vitreous and starchy endosperm of maize kernels is essential for texture ***,the complex internal structure of maize kernels presents several *** CT(computed tomog-raphy)images,the pixel intensity differences between the vitreous and starchy endosperm regions in maize kernel CT images are not distinct,potentially leading to low segmentation accuracy or ***,the blurred edges between the vitreous and starchy endosperm make segmentation difficult,often resulting in jagged segmentation *** propose a deep learning-based CT image analysis pipeline to examine the internal structure of maize ***,CT images are acquired using a multislice CT *** improve the efficiency of maize kernel CT imaging,a batch scanning method is *** kernels are accurately segmented from batch-scanned CT images using the Canny ***,we modify the conventional architecture for high-quality segmentation of the vitreous and starchy endosperm in maize *** conventional U-Net is modified by integrating the CBAM(convolutional block attention module)mechanism in the encoder and the SE(squeeze-and-excitation attention)mechanism in the decoder,as well as by using the focal-Tversky loss function instead of the Dice loss,and the boundary smoothing term is weighted as an additional loss term,named *** experimental results show that the CSFTU-Net model significantly improves the ability of segmenting vitreous and starchy ***,a segmented mask-based method is proposed to extract phenotype parameters of maize kernel texture,including the volume of the kernel(V),volume of the vitreous endosperm(VV),volume of starchy endosperm(SV),and ratios over their respective total kernel volumes(W/V and SV/V).The proposed pipeline facilitates the nondestructive quantification of the internal structure of maize kernels,offering valuable insights for maize breeding and processing.

关键词： Maize kernel Vitreous endosperm Starchy endosperm Semantic segmentation Mirco-CT

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Leveraging Machine Learning Algorithms to Optimize Diet Maintenance Based on User Food Intake and Fitness Data 5

Leveraging Machine Learning Algorithms to Optimize Diet Main...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Clarinda, K. Deepa, N. Dhamotharan, R. Krishnan, R. Santhana Raj, J. Relin Fransis Anchitaalagammai, J.V. SCAD College of Engineering and Technology Department of Management Studies Cheranmahadevi India PSNA College of Engineering and Technology Department of Artificial Intelligence and Data Science Dindigul India Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Krishnankoil India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798331530013

The increasing prevalence of lifestyle-related health issues has necessitated the development of innovative approaches to diet management. This research introduces the Adaptive Eating Pattern Predictor (AEPP), a novel machine learning algorithm designed to optimize diet maintenance by analyzing user food intake and fitness data. The AEPP combines the predictive capabilities of Artificial Neural Networks (ANN) with the probabilistic modeling of Markov Chains, enabling accurate forecasting of future eating patterns based on historical dietary and activity data. A comprehensive simulation analysis was conducted to evaluate the performance of the AEPP against existing algorithms, including traditional recommendation systems and basic machine learning models. Various simulation metrics, such as accuracy, precision, recall, and F1 score, were employed to compare the effectiveness of the AEPP in providing personalized dietary recommendations. The results demonstrate that the AEPP significantly outperforms existing algorithms, highlighting its potential as a valuable tool for individuals seeking to maintain a balanced diet in alignment with their fitness goals. This study contributes to the field of nutritional science by showcasing the advantages of integrating advanced machine learning techniques in personal diet management. © 2024 IEEE.

关键词： Adversarial machine learning

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EFFECTS OF CUTTING AND WELDING ON ELECTRIC STEELS' FERROMAGNETIC MATERIAL

EFFECTS OF CUTTING AND WELDING ON ELECTRIC STEELS' FERROMAGN...

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Agarhari, Abishek Singh, Thakur Meenu Balaji, N.V. Arul Antran Vijay, S. Borade, Monali Ravindra Department of Computer Science & Engineering Invertis University Uttar Pradesh Bareilly243123 India School of Mechenical Engineering Dev Bhoomi Uttarakhand University Uttarakhand 248007 India Department of Computer Science & Engineering School of Engineering & Technology K. R. Mangalam University Haryana Gurugram122103 India Department of Computer Science Karpagam Academy of Higher Education Coimbatore641021 India Department of Computer Science Engineering Karpagam College of Engineering Coimbatore641032 India AI & Ds Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350355338

This study examines the crucial function that silicon steels, sometimes referred to as electric steels, serve in electromagnetic applications like transformers and motors. The need of preserving ferromagnetic characteristics for effective energy exchange is emphasized in particular. In order to fill in knowledge gaps about the precise effects of the two processes on these materials, this investigation presents a methodology that emphasizes the significance of careful material selection, thorough recording of the cutting and welding procedures, and an organized method of measurement and *** study recognizes the broad implications for industrial, power distribution, and automotive sectors, where ferromagnetic properties are necessary for electrical equipment to remain reliable. The study attempts to provide understanding of the unique effects of the two processes on electric steels, suggesting mitigating measures via a thorough analysis of the body of current literature. The transparent and reproducible technique presented in this study contributes to our knowledge of how electric steels behave in various contexts. The ultimate goal of the study is to improve production methods and guarantee that electromagnetic gear operates as well it can in our environment of growing electricity. © 2024 IEEE.

关键词： Automotive industry

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Evaluating the Effectiveness of Object Detection Algorithms for Autonomous Parking

Evaluating the Effectiveness of Object Detection Algorithms ...

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2024 International Conference on computer, Electronics, Electrical engineering and their Applications, IC2E3 2024

作者： Sharma, Utkarsh Subramonian, Krishnan Dsouza, Aaron Sughosh, P. Savitha, G. Girisha, S. Manipal Institute of Technology Manipal Academy of Higher Education Department of Data Science and Computer Applications Manipal576104 India Manipal Institute of Technology Manipal Academy of Higher Education Department of Civil Engineering Manipal576104 India

ISBN: (纸本)9798350388534

In response to the challenges posed by increasing urbanization, traditional parking systems face numerous issues, such as inaccurate slot detection and manual tracking, which exacerbate traffic congestion and environmental strain. This paper presents an autonomous parking system as a solution to these challenges. By leveraging advanced object detection algorithms, including YOLO-NAS, YOLOv8, and DETR models, this system revolutionizes parking space identification, freeing drivers from the task of manual searching. Our research focused on identifying the most suitable object detection algorithm for our dataset and objectives. Utilizing the 'PKLot' Dataset, we trained and fine-tuned our models, ensuring consistency in hyperparameters across algorithms to isolate their impact on performance. We evaluated algorithmic efficacy using metrics like mean average precision (mAP) and latency, measuring accuracy in discerning occupied and vacant spaces alongside real-time processing efficiency. Our evaluation results highlight the performance differences among algorithms, guiding future research. This research advocates for an intelligent, autonomous approach to parking management, aligning with goals of optimizing operations and promoting sustainable urban development. © 2024 IEEE.

关键词： Traffic congestion

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Diabetes Prediction Using Machine Learning Algorithms and Hyperparameter Tuning for Expecting Mothers

Diabetes Prediction Using Machine Learning Algorithms and Hy...

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2023 IEEE World Conference on Applied Intelligence and Computing, AIC 2023

作者： Kumar, D. Manoj Deny, J. Nagaraj, P. Dhanveer, Kudithipudi Kumar, Vaddepally Punith Arthi, R. Srm Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Virudhunagar Krishnankoil India

ISBN: (纸本)9798350310061

Diabetes is a chronic disease that occurs when the blood glucose levels in the human body exceed the normal range. If left untreated, diabetes can lead to various health complications. To prevent such complications, early detection and management of diabetes are crucial. In this study, a novel system is proposed that uses hyperparameter tuning in addition to various machine learning algorithms for early detection of diabetes. This article also tunes the hyperparameters of the algorithms to optimize their performance. The results indicate that the Support Vector Machine algorithm provides the best fit for predicting diabetes. Hyperparameter tuning further improves the performance of this algorithm. Our approach can be used as a decision support tool for healthcare professionals to identify high-risk patients early on and provide appropriate care and management. Our study highlights the potential of machine learning algorithms and hyperparameter tuning for developing accurate and reliable predictive models for diabetes in expecting mothers. These models can aid healthcare professionals in taking preventive measures to improve maternal and child health outcomes. © 2023 IEEE.

关键词： Support vector machines

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An FPGA Implementation for CCSDS Standard High Speed Low Complexity SCPPM Decoder 9

An FPGA Implementation for CCSDS Standard High Speed Low Com...

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9th Symposium on Novel Photoelectronic Detection technology and Applications

作者： Huang, Xuming Wu, Xiaowei Li, Lintao Wang, Wenhao Yang, Lei Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9781510664432

We present a high-speed, low-complexity, CCSDS-compatible SCPPM decoder architecture. At the algorithmic level, we use a combination of low-complexity Max-Log-MAP and SF-Max-Log-MAP algorithm. For both outer code and inner code SISO decoding module, we halve the delay by using a bidirectional calculation structure. We adopt the parallel-edge pre-computation and propose a symbol splitting method to reduce the complexity of the inner APPM decoder. We implement our proposed SCPPM design for 16-PPM in the FPGA platform. The implemented decoder achieves a throughput rate of more than 800Mbits per second at 250MHz clock. Meanwhile, our proposed SS-APPM inner decoder has an 80% reduction in FPGA logic resource utilization compared to the conventional APPM inner decoder. © 2023 SPIE.

关键词： Field programmable gate arrays (FPGA)

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Leveraging Bayesian Networks for Enhanced Transparency in Financial Data Provenance Tracking

Leveraging Bayesian Networks for Enhanced Transparency in Fi...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Nithya, K. Selvan, Mary Gnanavel, V.K. Solainayagi, P. Venkatesh, K. Department of Electronics and Communication Engineering Chennai India R.M.K College of Engineering and Technology Department of Artificial Intelligence and Data Science Puduvoyal India Asan Memorial College of Engineering and Technology Department of Computer Science and Engineering Korea Republic of Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India Kalasalingam Academy of Research and Education Department of CSE India

ISBN: (纸本)9798331542344

This work describes a probabilistic model based on Bayesian Networks for improving the record-keeping process of financial data and, thus, increasing the traceability of such information. To enhance fraud control and data provenance, the proposed system captures the probabilistic relationships between financial variables to enhance the identification of anomalous behaviours. The validation of the system was performed on the large data set of financial transactions and the system claimed an accuracy of 91 percent. 5%, precision of 89.2%, and recognition of 87. A special ranking of 8% in fraud detection was achieved, which is higher than the Decision Tree model with 85.3%and Random Forest model with 88. 7%. The performance of the Bayesian Network in terms of tracing was evaluated with the results for a single transaction being 28 ms, and for a sequence of transactions being 134 ms which is slightly slower than the traditional method but the advantage of utilizing a BN is its good level of interpretability. The system also obtained a True Positive Rate (TPR) of 89 percent. 8%) False Positive Rate 5%. 1% more than its rivals which proves its efficiency in the detection of frauds. Worst case performance analysis revealed the fact that though the framework is highly efficient for small to medium amounts of data the time required for processing data increases with the increase in the size of the data. There is also an aspect of interpretability which The Bayesian Network received a Score of 9. by financial experts only, it received a 5/10 score mainly on issues regarding the organization's decision making which is transparent. These outcomes show that Bayesian Networks are a stable and understandable solution for elements of financial data traceability, especially in highly responsible financial systems as applied in ISE. Further research will be directed towards achieving higher scalability for large scale use. © 2024 IEEE.

关键词： Decentralized finance

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KSKV:Key-Strategy for Key-Value Data Collection with Local Differential Privacy

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computer Modeling in engineering & sciences 2024年第6期139卷 3063-3083页

作者： Dan Zhao Yang You Chuanwen Luo Ting Chen Yang Liu Artificial Intelligence Development Research Center Institute of Scientific and Technical Information of ChinaBeijing100038China Industry Development Department NSFOCUS Inc.BeijingChina School of Information Science and Technology Beijing Forestry UniversityBeijing100083China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu610054China Institute of Computing Technology China Academy of Railway Sciences Corporation LimitedBeijing10081China

In recent years,the research field of data collection under local differential privacy(LDP)has expanded its focus fromelementary data types to includemore complex structural data,such as set-value and graph ***,our comprehensive review of existing literature reveals that there needs to be more studies that engage with key-value data *** studies would simultaneously collect the frequencies of keys and the mean of values associated with each ***,the allocation of the privacy budget between the frequencies of keys and the means of values for each key does not yield an optimal utility *** the importance of obtaining accurate key frequencies and mean estimations for key-value data collection,this paper presents a novel framework:the Key-Strategy Framework forKey-ValueDataCollection under ***,theKey-StrategyUnary Encoding(KS-UE)strategy is proposed within non-interactive frameworks for the purpose of privacy budget allocation to achieve precise key frequencies;subsequently,the Key-Strategy Generalized Randomized Response(KS-GRR)strategy is introduced for interactive frameworks to enhance the efficiency of collecting frequent keys through group-anditeration *** strategies are adapted for scenarios in which users possess either a single or multiple key-value ***,we demonstrate that the variance of KS-UE is lower than that of existing *** claims are substantiated through extensive experimental evaluation on real-world datasets,confirming the effectiveness and efficiency of the KS-UE and KS-GRR strategies.

关键词： Key-value local differential privacy frequency estimation mean estimation data perturbation

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