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5th IEEE International Conference on Recent Trends in computer science and Technology, ICRTCST 2024

作者： Anitha Kumari, D. Siddiqui, Mohd Shuaib Dorbala, Rajesh Megala, R. Rao, Kamarajugadda Tulasi Vigneswara Srikanth Reddy, N. Vels Institute of Science Technology and Advanced Studies Department of MBA Chennai TamilNadu Pallavaram India University of Tabuk Faculty of Business Administration Department of Management Tabuk Saudi Arabia Lovely Professional University Mittal School of Business Jalandhar-Delhi G.T.Road Punjab Phagwara India Sri Eshwar College of Engineering Department of Computer and Communication Engineering Tamilnadu Coimbatore India NICMAR University NICMAR Business School Pune India Presidency University Department of School of Management Karnataka Bengaluru India

ISBN: (纸本)9798350351378

Responding to the ever-changing e-commerce scene by utilising deep learning models for CLV forecasting in a variety of contexts. Client lifetime value is an important KPI for companies since it shows how much money customers are projected to spend while they are a part of a company's network. Improved CLV model accuracy and predictive capacity are the goals of this research, which use deep learning techniques - specifically, neural networks. In order to understand how consumer data contains temporal linkages and how these links affect purchasing behaviour, this article will look at several architectures like LSTMs and RNNs. The has two goals: first, to improve CLV estimates by analysing past transactions in detail;and second, to provide useful information for developing targeted marketing campaigns and retention-oriented programmes. The outcomes help bring e-commerce analytics and deep learning together. © 2024 IEEE.

关键词： Long short-term memory

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Boosting Cybersecurity Effectiveness through Machine Learning for Proactive Detection and Mitigation of New Threats

Boosting Cybersecurity Effectiveness through Machine Learnin...

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International Conference on Advances in Information Technology (ICAIT)

作者： N. Duraimutharasan N.Venkatesvara Rao N. Poongavanam K.V. Kanimozhi S.P. Manikandan Faculty of Advanced Computing Sciences AMET UNIVERSITY Chennai Tamil Nadu India Department of Computer Science and Engineering J.N.N.INSTITUTE OF ENGINEERING Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India School of Engineering and Technology CMR UNIVERSITY Bengaluru Karnataka

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Cybersecurity still remains an all-time issue before the technological age as the enterprises continue to deal with the evolving nature of the threats arising every day. Traditional cybersecurity approaches are becoming deficient in the situation of constantly improving, swiftly mutating threats, which leads to the revision and refocus of the current approaches to the preventative reactivity type. For that, machine learning (ML) has come into the arena as a strong tool for improving cybersecurity capacity through the.. p roactive” recognition and the “mitigation” of unknown threats. The focus of this paper is the function of ML in cyber security. Here is considered anomaly detection, malware classification, threat intelligence analysis, and security form adversarial attacks. We conduct literature search, detail a novel research methodology embracing data acquisition, preprocessing, ML model training, and deployment. We present experiments versus conventional security approaches from different ML domains used confirming the superiority in cybersecurity of the presented models. Moreover, resistance to chicanery such as adversarial attacks and the models understandability are discussed. Next, research vector and innovation directions in the ML cybersecurity are highlighted. As a whole, this study provides the example of how ML can strengthen previous alliance strategies to cybersecurity and prevent the risks associated with interchanging cyber threats.

关键词： Deep learning Heuristic algorithms Prevention and mitigation Malware Data models Real-time systems computer crime Anomaly detection Monitoring Resilience

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Using NSCTS and DCNN, Rapid Prediction and Forecasting of Breast Cancer

Using NSCTS and DCNN, Rapid Prediction and Forecasting of Br...

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Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), International Conference on

作者： K. Seethalakshmi A. Bhagyalakshmi A. Sangeetha S. Vijitha P. Suhas Palukuri Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India

Utilizing the CAD system, which aids radiologists in various diagnostic procedures, a recent study has been conducted on the early identification of breast cancer. Next to lung cancer, breast cancer ranks among the most deadly diseases. Initially, detection is done based on screening tests done through Magnetic resonance imaging (MRI), subsequently Infrared thermography, and digital mammography (DM). Whenever a suspicion is raised, these tests might be recommended by the specialist. Region-based segmentation is used for the test picture in this work, while NSCTS (Non-subsample Contourlet transform segmentation) is used for preprocessing. ResNet-164 DCNN architecture performs feature extraction and classification between normal and aberrant tumor pictures. The diagnosis system adopts the deep learning technology to increase its performance rate. The approach to prediction starts with screening test photos, then preprocessing to enhance the image quality, ROI detection, ROI segmentation, and classification. The resulting images are examined using a variety of evaluation measures, including performance rate, sensitivity, specificity, and accuracy. When compared to other recent work, the outcome is exceptional. The obtained accuracy, sensitivity of the proposed system is 98.58% and 98.78%. The application takes advantage of the MIAS mammography image dataset.

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Vision-based Situational Graphs Exploiting Fiducial Markers for the Integration of Semantic Entities

arXiv

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arXiv 2023年

作者： Tourani, Ali Bavle, Hriday Sanchez-Lopez, Jose Luis Avşar, Deniz Işınsu Salinas, Rafael Muñoz Voos, Holger University of Luxembourg Luxembourg Institute for Advanced Studies University of Luxembourg Luxembourg Department of Physics & Materials Science University of Luxembourg Luxembourg Department of Computer Science and Numerical Analysis University of Córdoba Rabanales Campus Spain

Situational Graphs (S-Graphs) merge geometric models of the environment generated by Simultaneous Localization and Mapping (SLAM) approaches with 3D scene graphs into a multi-layered jointly optimizable factor graph. As an advantage, S-Graphs not only offer a more comprehensive robotic situational awareness by combining geometric maps with diverse hierarchically organized semantic entities and their topological relationships within one graph, but they also lead to improved performance of localization and mapping on the SLAM level by exploiting semantic information. In this paper, we introduce a vision-based version of S-Graphs where a conventional Visual SLAM (VSLAM) system is used for low-level feature tracking and mapping. In addition, the framework exploits the potential of fiducial markers (both visible as well as our recently introduced transparent or fully invisible markers) to encode comprehensive information about environments and the objects within them. The markers aid in identifying and mapping structural-level semantic entities, including walls and doors in the environment, with reliable poses in the global reference, subsequently establishing meaningful associations with higher-level entities, including corridors and rooms. However, in addition to including semantic entities, the semantic and geometric constraints imposed by the fiducial markers are also utilized to improve the reconstructed map’s quality and reduce localization errors. Experimental results on a real-world dataset collected using legged robots show that our framework excels in crafting a richer, multi-layered hierarchical map and enhances robot pose accuracy at the same time. © 2023, CC BY.

关键词： Mapping

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Estimating Multiple Socioeconomic Attributes via Home Location-A Case Study in China

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Journal of Social computing 2021年第1期2卷 71-88页

作者： Shichang Ding Xin Gao Yufan Dong Yiwei Tong Xiaoming Fu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou 276800China Department of Sociology Tsinghua UniversityBeijing 100085China Institute of Computer Science University of GottingenGottingen 37077Germany Shanghai Hejin Information Technology Company Shanghai 200100China

Inferring people’s Socioeconomic Attributes(SEAs),including income,occupation,and education level,is an important problem for both social sciences and many networked applications like targeted advertising and personalized *** works mainly focus on estimating SEAs from peoples’cyberspace behaviors and relationships,such as the content of tweets or the social networks between online *** cyberspace data,alternative data sources about users’physical behavior,like their home location,may offer new *** specifically,in this paper,we study how to predict a person’s income level,family income level,occupation type,and education level from his/her home *** a case study,we collect people’s home locations and socioeconomic attributes through a survey involving 9 provinces and 85 cities in *** further enrich home location with the knowledge from real estate websites,government statistics websites,online map services,*** learn a shared representation from input features as well as attribute-specific representations for different SEAs,we propose H2SEA,a factorization machine-based multi-task learning method with attention *** experiment results show that:(1)Home location can clearly improve the estimation accuracy for all SEA prediction tasks(e.g.,80.2%improvement in terms of F1-score in estimating personal income level);(2)The proposed H2SEA model outperforms alternative models for SEA inference in terms of various evaluation metrics,such as Area Under Curve(AUC),F-measure,and specificity;(3)The performance of specific SEA prediction tasks(e.g.,personal income)can be further improved if H2SEA only focuses on cities or villages due to urban-rural gap in China;(4)Compared with online crawled housing price data,the area-level average income and Points Of Interest(POI)are more important features for SEA inferences in China.

关键词： personal income family income occupation education multi-task learning

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OneForecast: A Universal Framework for Global and Regional Weather Forecasting

arXiv

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arXiv 2025年

作者： Gao, Yuan Wu, Hao Shu, Ruiqi Dong, Huanshuo Xu, Fan Chen, Rui Yan, Yibo Wen, Qingsong Hu, Xuming Wang, Kun Wu, Jiahao Li, Qing Xiong, Hui Huang, Xiaomeng Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University China TEG Tencent China Department and Computer and Science University of Science and Technology of China China Institute for Interdisciplinary Information Sciences Tsinghua University China Department of Computer Science and Engineering The HongKong University of Science and Technology Hong Kong AI Thrust The Hong Kong University of Science and Technology Guangzhou China Squirrel Ai Learning China School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computing The Hong Kong Polytechnic University Hong Kong

Accurate weather forecasts are important for disaster prevention, agricultural planning, and water resource management. Traditional numerical weather prediction (NWP) methods offer physically interpretable high-accuracy predictions but are computationally expensive and fail to fully leverage rapidly growing historical data. In recent years, deep learning methods have made significant progress in weather forecasting, but challenges remain, such as balancing global and regional high-resolution forecasts, excessive smoothing in extreme event predictions, and insufficient dynamic system modeling. To address these issues, this paper proposes a global-regional nested weather forecasting framework based on graph neural networks (GNNs). By combining a dynamic system perspective with multi-grid theory, we construct a multi-scale graph structure and densify the target region to capture local high-frequency features. We introduce an adaptive information propagation mechanism, using dynamic gating units to deeply integrate node and edge features for more accurate extreme event forecasting. For high-resolution regional forecasts, we propose a neural nested grid method to mitigate boundary information loss. Experimental results show that the proposed method performs excellently across global to regional scales and short-term to long-term forecasts, especially in extreme event predictions (e.g., typhoons), significantly improving forecast accuracy. Our codes are available at https://***/YuanGao-YG/OneForecast. Copyright © 2025, The Authors. All rights reserved.

关键词： Weather forecasting

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Harvesting Insights: Predicting Vegetable Prices with Hybrid CLA-Conv BiGRU Techniques

Harvesting Insights: Predicting Vegetable Prices with Hybrid...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： Kanahaiya Lal Ambashtha Chatakunta Praveen Kumar Vivekanand Pandey S. Sakthivel Padaiyatchi A. Bharathi K. Navaz Faculty of Information Technology GNSU Sasaram Bihar India Department of Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad India Amity University Patna Bihar India Department of Electrical and Electronics Engineering Nehru Institute of Engineering and Technology Coimbatore TN India Department of Information Technology Vels Institute of Science Technology and Advanced Studies Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai

The system delves into the important topic of agricultural market pricing, with a specific emphasis on the ever-changing realm of vegetable supply and prices. Stabilizing the supply and prices of vegetables becomes an enormous task when consider that the system are grown outdoors and that weather variables significantly impact harvests. Such unpredictability has far-reaching consequences for the country's economy, highlighting the critical need to develop efficient methods of precise forecasting. The government has made multiple attempts to stabilize vegetable prices and supplies, but these have all been unsuccessful due to the unpredictable weather patterns of late. This system uses state-of-the-art deep learning methods, notably the Hybrid CLA-Conv BiGRU model, which incorporates a novel preprocessing strategy based on the HP Filter, to conquer this obstacle. A strong prediction model for vegetable prices is constructed using the process, which includes feature extraction, scoring, and selection.

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Details Enhancement in Unsigned Distance Field Learning for High-fidelity 3D Surface Reconstruction 39

Details Enhancement in Unsigned Distance Field Learning for ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xu, Cheng Hou, Fei Wang, Wencheng Qin, Hong Zhang, Zhebin He, Ying State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China University of Chinese Academy of Sciences China School of Advanced Interdisciplinary Sciences University of Chinese Academy of Sciences China Department of Computer Science Stony Brook University United States InnoPeak Technology United States College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)157735897X

While Signed Distance Fields (SDF) are well-established for modeling watertight surfaces, Unsigned Distance Fields (UDF) broaden the scope to include open surfaces and models with complex inner structures. Despite their flexibility, UDFs encounter significant challenges in high-fidelity 3D reconstruction, such as non-differentiability at the zero level set, difficulty in achieving the exact zero value, numerous local minima, vanishing gradients, and oscillating gradient directions near the zero level set. To address these challenges, we propose Details Enhanced UDF (DEUDF) learning that integrates normal alignment and the SIREN network for capturing fine geometric details, adaptively weighted Eikonal constraints to address vanishing gradients near the target surface, unconditioned MLP-based UDF representation to relax non-negativity constraints, and DCUDF for extracting the local minimal average distance surface. These strategies collectively stabilize the learning process from unoriented point clouds and enhance the accuracy of UDFs. Our computational results demonstrate that DEUDF outperforms existing UDF learning methods in both accuracy and the quality of reconstructed surfaces. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Surface reconstruction

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Global Reliable Diagnosis of Networks Based on Self-Comparative Diagnosis Model and G-Good-Neighbor Property

SSRN

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SSRN 2024年

作者： Wang, Mujiangshan Xu, Shuhao Jiang, Jincheng Xiang, Dong Hsieh, Sun-Yuan Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Software Tsinghua University Beijing100084 China Department of Computer Science and Information Engineering National Cheng Kung University Tainan701 Taiwan Guangdong Academy of Agricultural Sciences China

In the research field of system-level diagnosis, people used to focus on studying different diagnosabilities with restrictions. By studying this, reliability measured by these diagnosabilities of various of networks is obtained. However, for the decentralized parallel computing systems, there are always upper bonds for diagnosabilities, which means there exists an undiagnosable subnetwork cut off by a faulty vertex cut if the subnetwork is large enough. In this paper, we set up a 2-stage plan for the global reliable diagnosis of networks combining Self-Comparative diagnosis (SC) model and the g-good-neighbor property, which means every vertex in the networks is diagnosed with high accuracy. Six algorithms are proposed for the realization of the plan and diagnosis model for g≥2. In the end, we code a simulator for the plan and test how well it works and the codes are provided via Github link for readers' check. © 2024, The Authors. All rights reserved.

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Optimally Tracking Labels on an Evolving Tree

arXiv

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arXiv 2022年

作者： Acharya, Aditya Mount, David M. Department of Computer Science University of Maryland College ParkMD United States Department of Computer Science Institute for Advanced Computer Studies University of Maryland College ParkMD United States

Motivated by the problem of maintaining data structures for a large sets of points that are evolving over the course of time, we consider the problem of maintaining a set of labels assigned to the vertices of a tree. We study the problem in the evolving data framework, where labels continuously change over time due to the action of an agent called the evolver. An algorithm, which can only track these changes by explicitly probing the individual vertices, is tasked with maintaining an approximate sketch of the underlying tree. Such a framework necessitates an algorithm which is fast enough to keep up with the changes, while simultaneously being accurate enough to maintain a close approximation. We present an algorithm that allows for both randomized and adversarial evolution of the data, subject to allowing a constant speedup factor over the evolver. We show that in the limit, it is possible to maintain labels to within an average distance of O(1) of their actual locations. We also present nearly matching lower bounds, both on the distance, and the speed-up factor. © 2022, CC BY.

关键词： Approximation algorithms

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