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International Conference on Inventive Computation Technologies (ICICT)

作者： K. Veena K. Meena Mercy Theresa M Hemalatha C D. Rajalakshmi Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computer Science and Engineering GITAM School of Technology GITAM- Bangalore India Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Computer Science and Engineering Sri Sairam Institute of Technology Chennai

Network security is a critical issue in modern technology. Honey pot-based intrusion detection methods provide an additional layer of security and enhance network performance by analyzing hacker behaviour and detecting unauthorized clients. IP validation and vulnerability detection are used to ensure authorized customer access and prevent illegal client activity. A voice recognition system detects malicious attacks in stage two. If authorized, packets are passed to the server, otherwise, they are delivered to honeypot servers. Using Naive Bayes algorithm, the detection rate is 85.06% with an accuracy of 98.18% and a false alarm rate of 0.15 seconds.

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Reduced clinical severity during 2022 Shanghai Spring epidemic of SARS-CoV-2 omicron BA.2 variant infection——an integrated account of virus pathogenicity and vaccination effectiveness

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National science Review 2024年第4期11卷 24-27页

作者： Xingyue Wu Yao Chen Kangli Cao Yao Shen Xueling Wu Yilin Yang Zhongshu Kuang Qingrun Li Zhenzhen Lu Yichen Jia Mian Shao Guorong Gu Xiangwei Wang Ye Yao Ying Wang Shaodie Chen Zhigao Yu Wei Wei Longfei Ding Lulu Lan Tianwen Gu Xiangyu Long Jian Sun Lingyu Xing Jiayuan Shen Yi Han Yue Luo Sucheng Mu Mengna Lin Xiaoyan Zhang Rong Zeng Jianqing Xu Guoping Zhao Lihong Huang Zhenju Song Department of Emergency Medicine Clinical Center for Bio-Therapy Department of Biostatistics and Department of UrologyShanghai Key Laboratory of Lung Inflammation and Injury Zhongshan Hospital Fudan University Department of Respiratory and Critical Care Medicine Shanghai Pudong Hospital Fudan University Department of Pulmonology Renji Hospital Shanghai Jiao Tong University School of Medicine Key Laboratory of Systems Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Shanghai Public Health Clinical Center Fudan University Department of Biostatistics School of Public Health Key Laboratory of Public Health Safety of Ministry of Education and Shanghai Institute of Infectious Disease and Biosecurity School of Public Health Fudan University Shanghai Institute of Immunology Department of Immunology and Microbiology Shanghai Institute of Virology Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of EducationSchool of Medicine Shanghai Jiao Tong University State Key Laboratory of Genetic Engineering Fudan Microbiome CenterSchool of Life Sciences Fudan University Bio-Med Big Data Center Shanghai Institute of Nutrition and Health Chinese Academy of Sciences

The emergence of SARS-Co V-2 variant of concern omicron BA.2 led to high infection incidence rates as well as reduced severity of COVID-19 during the Shanghai Spring epidemic of 2022 [1]. We assessed the role of viral pathogenicity and the effectiveness of inactivated vaccines in a retrospective observational study including 10 258 confirmed COVID-19cases identified between April and June of 2022, from four COVID-19 designated hospitals located in two areas of Shanghai, namely the West Bund and the East Bund, where the populations’ demographic characteristics varied significantly (Table S1, Fig. S1).

关键词： sars-cov-2 effectiveness epidemic shanghai integrated spring clinical variant virus account

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Oil Spill Detection Using 2D Convolution Neural Network and Generative Adversarial Network

Oil Spill Detection Using 2D Convolution Neural Network and ...

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Expert Clouds and Applications (ICOECA), International Conference on

作者： R. Madhura S. Nikkath Bushra A. Syed Ismail Haleema PK Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India School of Computing and Engineering University of West London RAK Branch Campus UAE

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

Finding oil spills in the ocean is one of the most crucial tasks in preserving our ecosystem. Using satellite or aerial photographs as input to a deep learning model that makes use of 2D CNN (Convolution Neural Network) and GAN (Generative Adversarial Network). Deep learning assists in automatically detecting oil spills in the ocean. A strong model that may be applied to volumetric data is 2D CNN. To protect the environment and its biodiversity, it is critical to identify and minimize oil spills in delicate maritime environments. Conventional techniques for tracking oil spills in satellite imagery sometimes depend on labor-intensive, human error-prone manual inspection. When deep learning models are taught with a lot of data, they perform well. The Gathering of datasets, however the situation proved to be challenging. As a result, employing Generative Adversarial Networks (GAN) to produce new photos by utilizing preexisting ones. As a result, by giving the model a suitable number of photos to use for training and testing, it improves accuracy.

关键词： Deep learning Training Convolution Oils Biological system modeling Oceans Neural networks

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Shielding NLP systems: An In-depth Survey on Advanced AI Techniques for Adversarial Attack Detection in Cyber Security

Shielding NLP Systems: An In-depth Survey on Advanced AI Tec...

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Automation, Computing and Renewable systems (ICACRS), International Conference on

作者： T. Sai Lalith Prasad K. B. Manikandan J. Vinoj Research Scholar Department of Computer Science and Engineering Vignan's Foundation for Science and Technology & Research (Deemed to be University) Guntur Andhra Pradesh India Assistant Professor Department of Artificial Intelligence and Data Science Vignan Institute of Technology and Science Telangana India Assistant Professor Department of CSE Vignan's Foundation for Science Technology and Research (Deemed to be University) Guntur Andhra Pradesh India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

This research investigates the challenges posed by adversarial perturbations to the robustness and reliability of Natural Language Processing (NLP) models. The study focuses on three key paradigms for enhancing the resilience of NLP systems: meta-learning, reinforcement learning, and self-supervised learning. A comprehensive evaluation of these strategies is conducted, analyzing their strengths and weaknesses in mitigating adversarial attacks. The study provides insights into the most promising approaches for enhancing the robustness of NLP models and offers directions for future research in this critical area. By advancing the resilience of NLP systems, this research contributes to the development of more robust and trustworthy AI applications across various domains, including healthcare, finance, and security.

关键词： Training Surveys Renewable energy sources Scalability Self-supervised learning Natural language processing Robustness Security Artificial intelligence Resilience

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Linear Temporal Logic-based Mixed-Integer Linear Problem Planning with the Koopman Operator 48

Linear Temporal Logic-based Mixed-Integer Linear Problem Pla...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Tokuda, Shumpei Yamakita, Masaki Oyama, Hiroyuki Takano, Rin Tokyo Institute of Technology Department of Systems and Control Engineering 1-11-1 Oh-Okayama Meguro151-8551 Japan Nec Corporation Data Science Research Laboratories 1753 Shimonumabe Nakahara-ku Kanagawa Kawasaki211-8666 Japan

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

We present a formulation for a linear temporal logic (LTL)-based task planning using the Koopman operator. The dynamics of nonlinear systems can be represented as linear systems by lifting them to a space of augmented states using the Koopman operator. On the other hand, the lifted linear system cannot capture the nonlinear effects of inputs, which appear in many robotic systems. Therefore, instead of a lifted linear system, we can consider representing control-affine bilinear systems. However, since the lifted bilinear systems are nonlinear, we need to solve nonlinear programming problems for trajectory optimization. This paper presents a methodology for the trajectory optimization problem of the lifted bilinear system. Using the mixed-integer convex approximation, we can solve the trajectory optimization problem of the lifted bilinear systems as a mixed-integer linear programming problem. This formulation allows us to solve LTL-based task planning problems for nonlinear systems. The effectiveness of the proposed method was confirmed by numerical simulations. © 2022 IEEE.

关键词： Trajectories

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An overview of ML-based applications for next generation optical networks

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science China(Information sciences) 2020年第6期63卷 31-46页

作者： Ruoxuan GAO Lei LIU Xiaomin LIU Huazhi LUN Lilin YI Weisheng HU Qunbi ZHUGE Shanghai Institute for Advanced Communication and Data Science State Key Laboratory of Advanced Optical Communication Systems and NetworksDepartment of Electronic Engineering Shanghai Jiao Tong University

Over the past few decades, the demand for the capacity and reliability of optical networks has continued to grow. In the meantime, optical networks with larger knowledge scales have become sources of numerous heterogeneous data. In order to handle these new challenges, many issues need to be resolved,among which the low-margin optical networks design, power optimization, routing and wavelength assignment(RWA), failure management are quite important. However, the use of traditional algorithms in the above four applications shows some shortcomings. Fortunately, artificial intelligence(AI), especially machine learning(ML), is regarded as one of the most promising methods to overcome these shortcomings. In this study, we review the applications of ML methods in solving these four issues. Although many ML-based researches have emerged, the applications of ML techniques in optical networks still face challenges. Therefore, we also discuss some possible future directions of investigating ML-based approaches in optical networks.

关键词： optical networks artificial intelligence machine learning system margin power optimization routing and wavelength assignment failure management

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Notice of Removal: Fourier Diffusion Models: A Method to Control MTF and NPS in Score-Based Stochastic Image Generation

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IEEE Transactions on Medical Imaging 2023年 PP卷 1-1页

作者： Tivnan, Matthew Teneggi, Jacopo Lee, Tzu-Cheng Zhang, Ruoqiao Boedeker, Kirsten Cai, Liang Gang, Grace J. Sulam, Jeremias Stayman, J. Webster Department of Biomedical Engineering Johns Hopkins University in Baltimore MD USA Department of Computer Science and Mathematical Institute for Data Science Johns Hopkins University in Baltimore MD USA Canon Medical Research USA in Vernon Hills IL USA Canon Medical Systems Corporation Otawara Japan Department of Radiology University of Pennsylvania in Philadelphia PA USA Department of Biomedical Engineering and Mathematical Institute for Data Science Johns Hopkins University in Baltimore MD USA

Removed.

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Multi-Behavior Graph Neural Networks for Recommender System

arXiv

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arXiv 2023年

作者： Xia, Lianghao Huang, Chao Xu, Yong Dai, Peng Bo, Liefeng The Department of Computer Science Musketeers Foundation Institute of Data Science University of Hong Kong Hong Kong The School of Computer Science and Technology South China University of Technology Guangzhou510006 China JD Silicon Valley Research Center 675 E Middlefield Rd Mountain ViewCA94043 United States

Recommender systems have been demonstrated to be effective to meet user’s personalized interests for many online services (e.g., E-commerce and online advertising platforms). Recent years have witnessed the emerging success of many deep learning-based recommendation models for augmenting collaborative filtering architectures with various neural network architectures, such as multi-layer perceptron and autoencoder. However, the majority of them model the user-item relationship with single type of interaction, while overlooking the diversity of user behaviors on interacting with items, which can be click, add-to-cart, tag-as-favorite and purchase. Such various types of interaction behaviors have great potential in providing rich information for understanding the user preferences. In this paper, we pay special attention on user-item relationships with the exploration of multi-typed user behaviors. Technically, we contribute a new multi-behavior graph neural network (MBRec), which specially accounts for diverse interaction patterns as well as the underlying cross-type behavior inter-dependencies. In the MBRec framework, we develop a graph-structured learning framework to perform expressive modeling of high-order connectivity in behavior-aware user-item interaction graph. After that, a mutual relation encoder is proposed to adaptively uncover complex relational structures and make aggregations across layer-specific behavior representations. Through comprehensive evaluation on real-world datasets, the advantages of our MBRec method have been validated under different experimental settings. Further analysis verifies the positive effects of incorporating the multi-behavioral context into the recommendation paradigm. Additionally, the conducted case studies offer insights into the interpretability of user multi-behavior representations. We release our model implementation at https://***/akaxlh/MBRec. Copyright © 2023, The Authors. All rights reserved.

关键词： Recommender systems

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Experimenting on Markov Decision Processes with Local Treatments

arXiv

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arXiv 2024年

作者： Chen, Shuze Simchi-Levi, David Wang, Chonghuan Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200030 China Institute for Data Systems and Society Department of Civil and Environmental Engineering Operations Research Center Massachusetts Institute of Technology CambridgeMA02139 United States Center for Computational Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States

Utilizing randomized experiments to evaluate the effect of short-term treatments on the short-term outcomes has been well understood and become the golden standard in industrial practice. However, as service systems become increasingly dynamical and personalized, much focus is shifting toward maximizing long-term cumulative outcomes, such as customer lifetime value, through lifetime exposure to interventions. To bridge this gap, we investigate the randomized experiments within dynamical systems modeled as Markov Decision Processes (MDPs). Our goal is to assess the impact of treatment and control policies on long-term cumulative rewards from relatively short-term observations. We first develop optimal inference techniques for assessing the effects of general treatment patterns. Furthermore, recognizing that many real-world treatments tend to be fine-grained and localized for practical efficiency and operational convenience, we then propose methods to harness this localized structure by sharing information on the non-targeted states. Our new estimator effectively overcomes the variance lower bound for general treatments while matching the more stringent lower bound incorporating the local treatment structure. Furthermore, our estimator can optimally achieve a linear reduction with the number of test arms for a major part of the variance. Finally, we explore scenarios with perfect knowledge of the control arm and design estimators that further improve inference efficiency. Copyright © 2024, The Authors. All rights reserved.

关键词： Efficiency

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Design and Comparison of Collaborative Filtering Technology for Product Suggestions in E-Commerce 1

Design and Comparison of Collaborative Filtering Technology ...

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1st IEEE International Conference on Automation, Computing and Renewable systems, ICACRS 2022

作者： Rajasekar, P. Mohanraj, B. Padhi, S.N. Sivakumar, N. Lavanya, J. Charles Prabu, V. Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Tamil Nadu Chennai603203 India Department of Information Technology Karpagam College of Engineering Tamil Nadu Coimbatore641032 India Department of Mechanical Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Department of Computer Science and Engineering Panimalar Engineering College Chennai City Campus Tamil Nadu Chennai600029 India Department of Fashion Designing SRM Institute of Science and Technology CSH Kattankulathur campus Tamil Nadu Chennai603203 India Department of Computer Science Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai600 062 India

ISBN: (纸本)9781665460842

E-commerce websites and internet purchasing have never been more necessary. Product recommendations are a key tactic employed by online buying platforms. This study compares two different filtering algorithms that are used in product recommendationson websites like Amazon and Flipkart. Reviews of electrical devices, user data, and timestamps were all gathered from Kaggle and put into a dataset. These specifics then undergo several preprocessing steps. The steps are data visualization, duplicate value analysis, missing value analysis, and datatype analysis. The exhibited data is then used to train and test the machine learning models. For this purpose, two machine learning models are developed using two different algorithms. The algorithms K-Nearest Neighbor(KNN) and Support Vector Decomposition (SVD) are employed. The models are then trained to acquire used in the prediction procedure after that. The best models for the prediction process are then determined by scrutinizing the results of both models. The Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE) values are located and compared because the findings of the analysis weren't *** erroneous data are plotted into a graph and evaluated to further demonstrate the point. As the SVD method is discovered to be the best of the group, the algorithm's greatest and worst predictions are discovered and examined. Even the poorest model constructed for the study's predictions turned out to be superior to several other prediction algorithms used for product suggestions. This algorithm can be deployed into the backend of any e-commerce website making it more effective. © 2022 IEEE

关键词： Value engineering

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