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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Chandre, Pankaj Shinde, Manoj Bapurao Tilwant, Shruti Vilas Ghandat, Arun Bhau Shendkar, Bhagyashree D Dhotre, Prashant Mit Art Design and Technology University Mit School of Computing Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798331518981

A Deep Q-Learning approach to Intrusion Detection and Prevention Systems (IDPS) offers a cutting-edge solution for enhancing cybersecurity by leveraging intelligent machine learning models. This method dynamically adapts to evolving threats, learning optimal defense strategies through a reward-punishment system. The presented architecture integrates sensor-based traffic monitoring, feature extraction, and real-time decision-making to strengthen network resilience. The system leverages sensor nodes to monitor traffic and uses traffic analyzers to extract critical features from network data. A Deep Q-Learning model is employed to analyze these features, dynamically adjusting security policies by learning through reward and punishment mechanisms. The system interacts with network infrastructure, such as firewalls, routers, and switches, to mitigate threats in real time. Threat intelligence is continuously updated in a dedicated database, while an action executor enforces security measures and policies. This intelligent adaptation ensures proactive threat prevention and network resilience. © 2024 IEEE.

关键词： Cybersecurity

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Women Infertility: A Systematic Review of Effects and Causes

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SN computer science 2023年第2期4卷 103页

作者： Sahana Devi, K.J. Yendapalli, Vamsidhar Department of Computer Science and Engineering GITAM School of Technology Bengaluru GITAM University Karnataka Bengaluru India

Recently, infertility has been affecting a large number of women than men. It is the male or female reproductive system’s disease. Consequently, after 12 months or more of usual insecure sexual intercourse, if they fail to attain pregnancy then that failure is defined as infertility. As per the National Institute of Child Health and Human Development, people in the USA have infertility issues that are found in 11% of women along with 9% of men. Women are more fertile in their 20s, while in their 30s it reduces to half of it. After 35 years, the probability of getting pregnant is diminished in women. For the woman to get pregnant, the proper functioning of the ovaries, fallopian tubes, and uterus is required. So, infertility occurs due to any issues with the above body parts. Women's infertility is caused by many factors, but the most vital cause is polycystic ovary syndrome (PCOS), which is a hormonal disorder commonly found among reproductive-aged women. So, women infertility, effects of women infertility, causes of women infertility, PCOS and detection of PCOS by general and machine learning (ML) techniques had been discussed in this paper. The accuracy attained in the detection of PCOS utilizing ML techniques is analyzed. Among people as of the top five responding countries that incorporate the USA, UK, Australia, India, together with the Philippines, the percentage of women with physician-assured PCOS against women exclusive of PCOS is analyzed. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Causes Detection of PCOS Effects ML techniques PCOS Women infertility

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Preoperative MR Image prediction of breast cancer based on self-supervised learning 23

Preoperative MR Image prediction of breast cancer based on s...

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2nd International Conference on Algorithms, Data Mining, and Information technology, ADMIT 2023

作者： Chen, Wenhan Sun, Fan Chen, Xuyuan Luo, Yi Liu, Miing School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Province Chengdu China University of Electronic Science and Technology of China Quzhou China

ISBN: (纸本)9798400707629

Breast cancer is a prevalent cancer type among women worldwide, which contributes to significant mortality and morbidity rates. Early detection of breast cancer plays a crucial role in improving patients' chances of survival. In this study, we utilized a publicly available dataset consisting of Magnetic Resonance (MR) images from 1,480 individuals. Leveraging deep learning technology, we successfully developed an accurate automatic detection system for breast cancer in MR images. To address the challenge of limited medical datasets, we propose a model based on self-supervised learning. This approach allows us to effectively overcome the limitations associated with small-scale datasets. Our experimental results demonstrate that our proposed model significantly enhances the predictive performance of breast cancer detection. Notably, our model achieved a remarkable six percentage point improvement in terms of AUC (reaching 0.95), when compared to the baseline model. The outcomes of this research present a promising step towards automated and precise prediction of breast cancer. The application of our model can aid in early-stage breast cancer detection, enabling timely treatment planning and ultimately enhancing patient survival rates. © 2023 ACM.

关键词： Magnetic resonance imaging

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Identification of High-Risk Areas for Geological Disasters using classification methods under complex environmental conditions 23

Identification of High-Risk Areas for Geological Disasters u...

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3rd International Conference on Artificial Intelligence, Automation and Algorithms, AI2A 2023

作者： Zhou, Jing-Wen Fu, Shao-Feng Li, Long-Hai Dong, Jun-Zhe School of Computer Science and Technology Xidian University China School of Cyber Engineering Xidian University China

ISBN: (纸本)9798400707605

Geological disasters result in significant human and property losses. It is imperative to identify areas prone to geological disasters for prevention and monitoring purposes. Identifying disaster-prone areas can be approached as a machine-learning classification problem. Various factors such as elevation, slope, aspect, terrain undulation, vegetation coverage, landform, and soil moisture content can be utilized as input variables for the classification algorithm to determine the probability of landslide occurrence within a specific range. Previous research in this area has been limited and fails to encompass diverse geological environments. This paper addresses this gap by utilizing a dataset collected within a province, which exhibits ample diversity and coverage. The dataset incorporates both continuous and discrete variables, allowing for a comprehensive evaluation of different classification algorithms. The primary objective of this study is to identify the most suitable classification algorithm for those conditions. By comparing the accuracy, ROC curves, and training duration of AdaBoost, cart, gbdt, xgboost, and random forest on multiple test datasets, it was determined that random forest yielded the best performance. The findings of this research can serve as a valuable reference for future related studies. © 2023 ACM.

关键词： Big data

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3D Face Alignment and Face Reconstruction Based on Image Sequence 14

3D Face Alignment and Face Reconstruction Based on Image Seq...

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14th International Conference on Digital Image Processing, ICDIP 2022

作者： Wei, Yao Qiao, Biao Wang, Huabin Zhang, Mengxin Liu, Shijun Tao, Liang Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei China School of Computer Science and Information Engineering Bengbu University Bengbu China

ISBN: (纸本)9781510657564

Existing 3D face alignment and face reconstruction methods mainly focus on the accuracy of the model. When the existing methods are applied to dynamic videos, the stability and accuracy are significantly reduced. To overcome this problem, we propose a novel regression framework that strikes a balance between accuracy and stability. First, on the basis of lightweight backbone, encoder-decoder structure is used to jointly learn expression details and detailed 3D face from video images to recover shape details and their relationship to facial expression, and dynamic regression of a small number of 3D face parameters, effectively improve the speed and accuracy. Secondly, in order to further improve the stability of face landmarks in video, a jitter loss function of multi-frame image joint learning is proposed to strengthen the correlation between frames and face landmarks in video, and reduce the difference amplitude of face landmarks between adjacent frames to reduce the jitter of face landmarks. Experiments on several challenging datasets verify the effectiveness of our method. © 2022 SPIE.

关键词： Jitter

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Enhancing 3D Modeling Quality Through Orthorectified Image Accumulation

Enhancing 3D Modeling Quality Through Orthorectified Image A...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Kwag, Cheolsu Kim, Euntaek Lee, Yongsuk Jung, Seungmin Hwang, Sung Soo Ryu, Hwangryol Moon, Youngmin Handong Global University Dept. of Computer Science and Electrical Engineering Pohang Korea Republic of School of Computer Science and Electrical Engineering Handong Global University Pohang Korea Republic of Research Institute of Industrial Science and Technology Smart Engineering Research Laboratory Pohang Korea Republic of

ISBN: (纸本)9798331510756

This study introduces a novel methodology designed to facilitate the capture of comprehensive image datasets, crucial for accurate 3D modeling of expansive indoor spaces. Leveraging orthophotos generated from panoramic camera data and visualizing image coverage density through heatmaps, our approach effectively identifies under-captured and unvisited areas. This enables targeted image acquisition, minimizes redundant reshoots, and enhances the overall quality and fidelity of 3D models. © 2025 IEEE.

关键词： Digital elevation model

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LQTTrack:Multi-Object Tracking by Focusing on Low-Quality Targets Association

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computers, Materials & Continua 2024年第10期81卷 1449-1470页

作者： Suya Li Ying Cao Hengyi Ren Dongsheng Zhu Xin Xie Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information EngineeringHenan UniversityKaifeng475001China College of Information Science and Technology&Artificial Intelligence Nanjing Forestry UniversityNanjing210037China School of Computer Nanjing University of Posts and TelecommunicationsNanjing210023China

Multi-object tracking(MOT)has seen rapid improvements in recent ***,frequent occlusion remains a significant challenge in MOT,as it can cause targets to become smaller or disappear entirely,resulting in lowquality targets,leading to trajectory interruptions and reduced tracking *** from some existing methods,which discarded the low-quality targets or ignored low-quality target ***,with a lowquality association strategy(LQA),is proposed to pay more attention to low-quality *** the association scheme of LQTTrack,firstly,multi-scale feature fusion of FPN(MSFF-FPN)is utilized to enrich the feature information and assist in subsequent data ***,the normalized Wasserstein distance(NWD)is integrated to replace the original Inter over Union(IoU),thus overcoming the limitations of the traditional IoUbased methods that are sensitive to low-quality targets with small sizes and enhancing the robustness of low-quality target ***,the third association stage is proposed to improve the matching between the current frame’s low-quality targets and previously interrupted trajectories from earlier frames to reduce the problem of track fragmentation or error tracking,thereby increasing the association success rate and improving overall multi-object tracking *** experimental results demonstrate the competitive performance of LQTTrack on benchmark datasets(MOT17,MOT20,and DanceTrack).

关键词： Low-quality targets association strategy feature fusion multi-object tracking tracking-by-detection

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Lithological Facies Classification Using Attention-Based Gated Recurrent Unit

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Tsinghua science and technology 2024年第4期29卷 1206-1218页

作者： Yuwen Liu Yulan Zhang Xingyuan Mao Xucheng Zhou Jingwen Chang Wenwei Wang Pan Wang Lianyong Qi College of Computer Science and Technology China University of Petroleum(East China)Qingdao 266580China Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyShouguang 262700China School of Mechanical Electrical and Information EngineeringShandong UniversityWeihai 264209China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of JinanJinan 250022China School of Modern Posts Nanjing University of Posts and TelecommunicationsNanjing 210023China

Lithological facies classification is a pivotal task in petroleum geology, underpinning reservoir characterization and influencing decision-making in exploration and production operations. Traditional classification methods, such as support vector machines and Gaussian process classifiers, often struggle with the complexity and nonlinearity of geological data, leading to suboptimal performance. Moreover, numerous prevalent approaches fail to adequately consider the inherent dependencies in the sequence of measurements from adjacent depths in a well. A novel approach leveraging an attention-based gated recurrent unit (AGRU) model is introduced in this paper to address these challenges. The AGRU model excels by exploiting the sequential nature of well-log data and capturing long-range dependencies through an attention mechanism. This model enables a flexible and context-dependent weighting of different parts of the sequence, enhancing the discernment of key features for classification. The proposed method was validated on two publicly available datasets. Results demonstrate a considerably improvement over traditional methods. Specifically, the AGRU model achieved superior performance metrics considering precision, recall, and F1-score.

关键词： attention mechanism facies classification GRU MLP(multilayer perceptron)

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A review of intelligent optimization for group scheduling problems in cellular manufacturing

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Frontiers of engineering Management 2023年第3期10卷 406-426页

作者： Yuting WANG Yuyan HAN Dunwei GONG Huan LI School of Computer Science Liaocheng UniversityLiaocheng 252059China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China

Given that group technology can reduce the changeover time of equipment,broaden the productivity,and enhance the flexibility of manufacturing,especially cellular manufacturing,group scheduling problems(GSPs)have elicited considerable attention in the academic and industry practical *** are two issues to be solved in GSPs:One is how to allocate groups into the production cells in view of major setup times between groups and the other is how to schedule jobs in each *** a number of studies on GSPs have been published,few integrated reviews have been conducted so far on considered problems with different constraints and their optimization *** this end,this study hopes to shorten the gap by reviewing the development of research and analyzing these *** literature is classified according to the number of objective functions,number of machines,and optimization *** classical mathematical models of single-machine,permutation,and distributed flowshop GSPs based on adjacent and position-based modeling methods,respectively,are also *** but not least,outlooks are given for outspread problems and problem algorithms for future research in the fields of group scheduling.

关键词： cellular manufacturing group scheduling flowshop literature review

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Advancing Horizons: Exploring the Aerospace IoT Revolution - Analysis, Advancements, and Prospects for the Future 3

Advancing Horizons: Exploring the Aerospace IoT Revolution -...

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Rajora, Ricky Rajora, Aarju Sharma, Bhanu Kumar, Gotte Ranjith Aggarwal, Priyanshi Thapliyal, Siddhant Chitkara University Institute of Engineering and Technology Chitkara University Punjab 140401 India Chandigarh Engineering College Department of Computer Science & Engineering Mohali Jhanjeri India Sr University School of Computer Science & Artificial Intelligence Warangal India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350381931

This study explores the profound influence of the Internet of Things (IoT) on the aerospace sector, examining the complex interplay between aircraft and IoT technology. This research examines the potential advantages and obstacles associated with the use of IoT technology in the aerospace industry. The primary areas of interest are improved operational effectiveness, heightened safety measures, and more scope for creativity. The use of IoT technology in the aviation industry has seen a notable rise. This has led to the widespread deployment of sensors that collect real-time data pertaining to diverse factors, including engine functionality and the in-flight experiences of passengers. The paper presents a thorough examination of Aerospace IoT, highlighting its significant impact on several areas within the aerospace industry. The objective of this project is to provide significant contributions to scholars, industry experts, policymakers, and technologists by offering important insights on the transformative capabilities of Aerospace IoT. © 2024 IEEE.

关键词： Internet of things

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