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A Hybrid Deep Learning-Based Unsupervised Anomaly Detection in High Dimensional data

computers, Materials & Continua 2022年第3期70卷 5363-5381页

作者： Amgad Muneer Shakirah Mohd Taib Suliman Mohamed Fati Abdullateef O.Balogun Izzatdin Abdul Aziz Department of Computer and Information Sciences Universiti Teknologi PETRONASSeri Iskandar32160Malaysia Centre for Research in Data Science(CERDAS) Universiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Information Systems Department Prince Sultan UniversityRiyadh11586Saudi Arabia

Anomaly detection in high dimensional data is a critical research issue with serious implication in the real-world *** issues in this field still unsolved,so several modern anomaly detection methods struggle to maintain adequate accuracy due to the highly descriptive nature of big *** a phenomenon is referred to as the“curse of dimensionality”that affects traditional techniques in terms of both accuracy and ***,this research proposed a hybrid model based on Deep Autoencoder Neural Network(DANN)with five layers to reduce the difference between the input and *** proposed model was applied to a real-world gas turbine(GT)dataset that contains 87620 columns and 56 *** the experiment,two issues have been investigated and solved to enhance the *** first is the dataset class imbalance,which solved using SMOTE *** second issue is the poor performance,which can be solved using one of the optimization *** optimization algorithms have been investigated and tested,including stochastic gradient descent(SGD),RMSprop,Adam and ***,Adamax optimization algorithm showed the best results when employed to train *** experimental results show that our proposed model can detect the anomalies by efficiently reducing the high dimensionality of dataset with accuracy of 99.40%,F1-score of 0.9649,Area Under the Curve(AUC)rate of 0.9649,and a minimal loss function during the hybrid model training.

关键词： Anomaly detection outlier detection unsupervised learning autoencoder deep learning hybrid model

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Improving Audience Ratings Prediction of Japanese TV Dramas Using Knowledge-Based Embeddings 23

Improving Audience Ratings Prediction of Japanese TV Dramas ...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Bonnell, Jerry Wuchty, Stefan Ogihara, Mitsunori Frost Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States University of Miami Department of Computer Science Coral GablesFL33146 United States

ISBN: (纸本)9798350374889

Accurate prediction of television (TV) audience ratings is a crucial technology for advertisers and broadcast-ers, which influences advertising costs and indicates program popularity. As for Japanese TV dramas, previous studies have examined factors available prior to broadcast, such as cast com-position, metadata variables (e.g., scheduled time slot, broadcasting station, episode duration), and indicators of actor popularity. However, such studies have been limited in their evaluation of contextual information related to these factors and have not considered additional modalities such as text data. Here, we propose the use of knowledge-based embeddings derived from Japanese Wikipedia to improve audience ratings prediction. The proposed bag-of-entities method is applied to represent dramas in terms of cast, production team, and plot synopsis elements, and is evaluated against conventional metadata features. In an ablation study, supervised forecasting models using Support Vector Machine (SVM) are trained on combinations of the feature groups. These are devised and tested on viewership data collected from the Audience Rating TV (ARTV) database for 574 Japanese TV dramas aired between 2003 and 2021. Our experimental results indicate that a SVM model conditioned on the full feature set outperforms baseline methods, achieving a 77.4 % F1-score. After stratifying predictions based on time slot popularity, the approach is found to be effective for predicting audience ratings in challenging, high-popularity time slots, with improvements in recognition rate up to 19.1 %. © 2024 IEEE.

关键词： Support vector machines

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Utilizing the simple graph convolutional neural network as a model for simulating influence spread in networks

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Computational Social Networks 2021年第1期8卷 1-17页

作者： Mantzaris, Alexander V. Chiodini, Douglas Ricketson, Kyle Department of Statistics and Data Science University of Central Florida (UCF) 4000 Central Florida Blvd Orlando 32816 United States Department of Computer Science University of Central Florida (UCF) 4000 Central Florida Blvd Orlando 32816 United States

The ability for people and organizations to connect in the digital age has allowed the growth of networks that cover an increasing proportion of human interactions. The research community investigating networks asks a range of questions such as which participants are most central, and which community label to apply to each member. This paper deals with the question on how to label nodes based on the features (attributes) they contain, and then how to model the changes in the label assignments based on the influence they produce and receive in their networked neighborhood. The methodological approach applies the simple graph convolutional neural network in a novel setting. Primarily that it can be used not only for label classification, but also for modeling the spread of the influence of nodes in the neighborhoods based on the length of the walks considered. This is done by noticing a common feature in the formulations in methods that describe information diffusion which rely upon adjacency matrix powers and that of graph neural networks. Examples are provided to demonstrate the ability for this model to aggregate feature information from nodes based on a parameter regulating the range of node influence which can simulate a process of exchanges in a manner which bypasses computationally intensive stochastic simulations. © 2021, The Author(s).

关键词： Graph convolutional neural networks Information diffusion Machine learning Networks Social influence Social networks

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Real-Time Intelligent Information Protection Using AI and Machine Learning Model 8

Real-Time Intelligent Information Protection Using AI and Ma...

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8th IEEE International Conference on science, Technology, Engineering and Mathematics, ICONSTEM 2023

作者： Anusha, P. Beatrice Dorothy, A. Kamalraj, N. Pundir, Sumit Verma, Shanti Jakka, Geethamanikanta R.M.K. Engineering College Department of Electronics and Communication Engineering Tamilnadu India Department of Data Science Tiruchirappalli620002 India Suguna College of Arts and Science Department of Computer Science Tamilnadu Coimbatore India Graphic Era Deemed to Be University Department of Computer Science Engineering Uttrakhand Dehradun India Lj University Department of Computer Applications Gujarat Ahmedabad India University of the Cumberlands Department of Information Technology FremontCA United States

ISBN: (纸本)9798350347791

Machine learning (ML) and Artificial intelligence (AI) are crucial components in the field of information security due to their capacity for rapid analysis of millions of events and identification of a wide variety of threats. Malware that takes use of zero-day vulnerabilities is just one type of cyber threat;others include detecting potentially dangerous behavior before it results in a phishing attempt or the download of malicious *** and ML are indispensable because of their capacity for rapid analysis of millions of even. Through time and experience, these systems have the ability to recognize previously unseen threats. Users, assets, and networks may all be profiled based on their behavior histories, giving AI the ability to spot and react to outliers. © 2023 IEEE.

关键词： HTTP

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Smart Agriculture System 4th

Smart Agriculture System

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4th International Conference on computer Vision, High-Performance Computing, Smart Devices, and Networks, CHSN 2023

作者： Kanaujia, Vishal Kumar Sah, Chitranjan Prasad Computer Science and Engineering-Data Science Department ABES Engineering College Uttar Pradesh Ghaziabad India School of Engineering and Technology Noida International University Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819777938

Agriculture stands as a linchpin for national prosperity, especially in regions blessed with abundant water and fertile soil, capable of yielding a diverse range of food products crucial for human sustenance. However, escalating global population and diminishing freshwater reserves pose significant challenges to sustainable agriculture. Approximately, 96% of Earth's water is saline, leaving a mere 3% as freshwater resources, intensifying the need for advanced techniques to conserve water in farming practices. This research addresses the pressing issue by proposing a solution rooted in smart agriculture, leveraging modern technologies like the Internet of Things (IoT) to revolutionize conventional farming methods. The integration of automation and IoT technologies, such as the ESP8266 IoT automatic irrigation system, is presented as a means to enhance crop productivity. This system utilizes cutting-edge technologies like Arduino and wireless sensor networks, offering a comprehensive solution for monitoring environmental conditions crucial for maximizing crop yield. The paper underscores the paramount importance of connecting agriculture to the Internet, emphasizing the role of IoT in facilitating applications like crop monitoring and automated irrigation decisions. The proposed smart agriculture system, featuring energy autonomy and cost-effectiveness, aims to address persistent agricultural challenges that impede national development. The research introduces a novel approach to agriculture, offering a tangible pathway toward sustainable farming practices in the face of water scarcity and environmental uncertainties. Through the integration of sensors and live data feeds, accessible via mobile devices, this system represents a promising step toward a technologically advanced and efficient agricultural landscape, especially beneficial for water-scarce and geographically isolated regions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 20

关键词： Smart agriculture

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NEARLY MINIMAX OPTIMAL OFFLINE REINFORCEMENT LEARNING WITH LINEAR FUNCTION APPROXIMATION: SINGLE-AGENT MDP AND MARKOV GAME 11

NEARLY MINIMAX OPTIMAL OFFLINE REINFORCEMENT LEARNING WITH L...

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11th International Conference on Learning Representations, ICLR 2023

作者： Xiong, Wei Zhong, Han Shi, Chengshuai Shen, Cong Wang, Liwei Zhang, Tong Department of Mathematics The Hong Kong University of Science and Technology Hong Kong Center for Data Science Peking University China Department of Electrical and Computer Engineering University of Virginia United States National Key Laboratory of General Artificial Intelligence Peking University China School of Intelligence Science and Technology Peking University China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

Offline reinforcement learning (RL) aims at learning an optimal strategy using a pre-collected dataset without further interactions with the environment. While various algorithms have been proposed for offline RL in the previous literature, the minimax optimality has only been (nearly) established for tabular Markov decision processes (MDPs). In this paper, we focus on offline RL with linear function approximation and propose a new pessimism-based algorithm for offline linear MDP. At the core of our algorithm is the uncertainty decomposition via a reference function, which is new in the literature of offline RL under linear function approximation. Theoretical analysis demonstrates that our algorithm can match the performance lower bound up to logarithmic factors. We also extend our techniques to the two-player zero-sum Markov games (MGs), and establish a new performance lower bound for MGs, which tightens the existing result, and verifies the nearly minimax optimality of the proposed algorithm. To the best of our knowledge, these are the first computationally efficient and nearly minimax optimal algorithms for offline single-agent MDPs and MGs with linear function approximation. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Reinforcement learning

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Exploring the Effect of Activation Function on Transformer Model Performance for Official Announcement Translator from Indonesian to Sundanese Languages

Exploring the Effect of Activation Function on Transformer M...

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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Wijanarko, Bambang Dwi Fitria Murad, Dina Heryadi, Yaya Tho, Cuk Hashimoto, Kiyota BINUS Online Learning Computer Science Department Jakarta11480 Indonesia BINUS Online Learning Information Systems Department Jakarta11480 Indonesia BINUS Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Prince of Songkla University Interdisciplinary Graduate School or Earth System Science and Andaman Natural Disaster Management Phuket Campus Phuket83120 Thailand

ISBN: (纸本)9798350320954

Automated language translation involving low-resource language has gained wide interest from many research communities in the past decade. One lesson learned from the past COVID-19 pandemic, particularly in Indonesia, is that many local Governments have to release regular public announcements to keep people following health protocol especially when they are in public areas. Many studies showed some evidence that rural people in Indonesia which covers a large proportion of Indonesia's population, feel more convenience receiving official announcements in their local language. However, translating official announcement from the national language to many local languages in Indonesia require many experienced bilingual translators and time. This paper presents exploration results in developing an automated language translator model to translate texts in Bahasa Indonesia to the Sundanese language. In particular, this study aims to explore the effect of ReLU, Sigmoid, and Tanh activation functions of the Vanilla Transformer Model on its translation performance. The experiment results showed that the activation function under study gives similar training accuracy (0.98). However, ReLU achieves better performance than Tanh in terms of validation accuracy, training loss, and validation loss. © 2023 IEEE.

关键词： Chemical activation

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The collaborative strategy of multiple USVs with deep reinforcement learning method 18

The collaborative strategy of multiple USVs with deep reinfo...

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18th International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2019

作者： Wu, Xing Zhong, Mingyu Feng, Guofei Xie, Shaorong Guo, Yike School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Institute for Advanced Communication and Data Science Shanghai China Department of Computing Imperial College London London United Kingdom

ISBN: (纸本)9781643680125

The unmanned surface vehicle (USV) has been widely used to accomplish tasks that cannot be completed by ships with human drivers on certain sea areas. It is not only necessary but essential to obtain a robust strategy in order to ensure multiple USVs accomplish collaborative tasks successfully and efficiently. To meet the challenge, a deep reinforcement learning method is proposed, which is combined with an improved A star algorithm. A statistically promising collaborative strategy is achieved by the proposed method under the guidance from the unmanned aerial vehicles (UAVs). After the collaborative strategy is generated, the improved A star algorithm is used to navigate the USVs. To verify the proposed algorithm, several tasks are tested on a simulation platform. Experimental results demonstrate that the proposed method outperforms state-of-the-art reinforcement learning methods such as DQN and DeepSarsa. © 2019 The authors and IOS Press. All rights reserved.

关键词： Reinforcement learning

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Inductive Relation Prediction by Disentangled Subgraph Structure

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Tsinghua science and Technology 2024年第5期29卷 1566-1579页

作者： Guiduo Duan Rui Guo Wenlong Luo Guangchun Luo Tianxi Huang Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of ChinaChengdu 611731Chinaand also with Trusted Cloud Computing and Big Data Key Laboratory of Sichuan ProvinceChengdu 611731China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Hangzhou NetEase Cloud Music Technology Co. Ltd.Hangzhou 310052China Department of Fundamental Courses Chengdu Textile CollegeChengdu 611700China

Currently,most existing inductive relation prediction approaches are based on subgraph structures,with subgraph features extracted using graph neural networks to predict ***,subgraphs may contain disconnected regions,which usually represent different semantic *** not all semantic information about the regions is helpful in relation prediction,we propose a relation prediction model based on a disentangled subgraph structure and implement a feature updating approach based on relevant semantic *** indirectly achieve the disentangled subgraph structure from a semantic perspective,the mapping of entity features into different semantic spaces and the aggregation of related semantics on each semantic space are *** disentangled model can focus on features having higher semantic relevance in the prediction,thus addressing a problem with existing approaches,which ignore the semantic differences in different subgraph ***,using a gated recurrent neural network,this model enhances the features of entities by sorting them by distance and extracting the path information in the ***,it is shown that when there are numerous disconnected regions in the subgraph,our model outperforms existing mainstream models in terms of both Area Under the Curve-Precision-Recall(AUC-PR)and Hits@*** prove that semantic differences in the knowledge graph can be effectively distinguished and verify the effectiveness of this method.

关键词： disentangled subgraph structure knowledge graph completion Gated Recurrent Unit(GRU) feature updating

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Federated Model Heterogeneous Matryoshka Representation Learning 38

Federated Model Heterogeneous Matryoshka Representation Lear...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yi, Liping Yu, Han Ren, Chao Wang, Gang Liu, Xiaoguang Li, Xiaoxiao College of Computer Science TMCC SysNet DISSec GTIISC Nankai University China College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering The University of British Columbia Canada Vector Institute Canada

Model heterogeneous federated learning (MHeteroFL) enables FL clients to collaboratively train models with heterogeneous structures in a distributed fashion. However, existing MHeteroFL methods rely on training loss to transfer knowledge between the client model and the server model, resulting in limited knowledge exchange. To address this limitation, we propose the Federated model heterogeneous Matryoshka Representation Learning (FedMRL) approach for supervised learning tasks. It adds an auxiliary small homogeneous model shared by clients with heterogeneous local models. (1) The generalized and personalized representations extracted by the two models' feature extractors are fused by a personalized lightweight representation projector. This step enables representation fusion to adapt to local data distribution. (2) The fused representation is then used to construct Matryoshka representations with multi-dimensional and multi-granular embedded representations learned by the global homogeneous model header and the local heterogeneous model header. This step facilitates multi-perspective representation learning and improves model learning capability. Theoretical analysis shows that FedMRL achieves a O(1/T) non-convex convergence rate. Extensive experiments on benchmark datasets demonstrate its superior model accuracy with low communication and computational costs compared to seven state-of-the-art baselines. It achieves up to 8.48% and 24.94% accuracy improvement compared with the state-of-the-art and the best same-category baseline, respectively. © 2024 Neural information processing systems foundation. All rights reserved.

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