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Future Technologies Conference, FTC 2020

作者： Adewole, Sodiq Yeghyayan, Michelle Hyatt, Dylan Ehsan, Lubaina Jablonski, James Copland, Andrew Syed, Sana Brown, Donald Department of Systems and Information Engineering University of Virginia CharlottesvilleVA United States School of Data Science University of Virginia CharlottesvilleVA United States Department of Pediatrics School of Medicine University of Virginia CharlottesvilleVA United States

ISBN: (纸本)9783030631277

Video capsule endoscope (VCE) is an emerging technology that allows examination of the entire gastrointestinal (GI) tract with minimal invasion. While traditional endoscopywith biopsy procedures are the gold standard for diagnosis of most GI diseases, they are limited by how far the scope can be advanced in the tract and are also invasive. VCE allows gastroenterologists to investigate GI tract abnormalities in detail with visualization of all parts of the GI tract. It captures continuous real time images as it is propelled in the GI tract by gut motility. Even though VCE allows for thorough examination, reviewing and analyzing up to eight hours of images (compiled as videos) is tedious and not cost effective. In order to pave way for automation of VCE-based GI disease diagnosis, detecting the location of the capsule would allow for a more focused analysis as well as abnormality detection in each region of the GI tract. In this paper, we compared four deep Convolutional Neural Network models for feature extraction and detection of the anatomical part within the GI tract captured by VCE images. Our results showed that VGG-Net has superior performance with the highest average accuracy, precision, recall and, F1-score compared to other state of the art architectures: GoogLeNet, AlexNet and, ResNet. © 2021, Springer Nature Switzerland AG.

关键词： Convolutional neural networks

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$\mathsf{PF\text{-}HIN}$:Pre-Training for Heterogeneous information Networks

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IEEE Transactions on Knowledge and data engineering 2022年第8期35卷 8372-8385页

作者： Yang Fang Xiang Zhao Yifan Chen Weidong Xiao Maarten de Rijke Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha Hunan China Laboratory for Big Data and Decision National University of Defense Technology Changsha Hunan China University of Amsterdam Amsterdam WX Netherlands

In network representation learning we learn how to represent heterogeneous information networks in a low-dimensional space so as to facilitate effective search, classification, and prediction solutions. Previous network representation learning methods typically require sufficient task-specific labeled data to address domain-specific problems. The trained model usually cannot be transferred to out-of-domain datasets. We propose a self-supervised pre-training and fine-tuning framework, $\mathsf{PF\text{-}HIN}$ , to capture the features of a heterogeneous information network. Unlike traditional network representation learning models that have to train the entire model all over again for every downstream task and dataset, $\mathsf{PF\text{-}HIN}$ only needs to fine-tune the model and a small number of extra task-specific parameters, thus improving model efficiency and effectiveness. During pre-training, we first transform the neighborhood of a given node into a sequence. $\mathsf{PF\text{-}HIN}$ is pre-trained based on two self-supervised tasks, masked node modeling and adjacent node prediction. We adopt deep bi-directional transformer encoders to train the model, and leverage factorized embedding parameterization and cross-layer parameter sharing to reduce the parameters. In the fine-tuning stage, we choose four benchmark downstream tasks, i.e., link prediction, similarity search, node classification, and node clustering. $\mathsf{PF\text{-}HIN}$ outperforms state-of-the-art alternatives on each of these tasks, on four datasets.

关键词： Task analysis Representation learning Computational modeling Transformers Training Predictive models Transforms

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Near-Optimal Algorithms for Constrained k-Center Clustering with Instance-level Background Knowledge

arXiv

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arXiv 2024年

作者： Guo, Longkun Jia, Chaoqi Liao, Kewen Lu, Zhigang Xue, Minhui School of Mathematics and Statistics Fuzhou University China School of Accounting Information Systems and Supply Chain RMIT University Australia HilstLab Peter Faber Business School Australian Catholic University Australia College of Science and Engineering James Cook University Australia CSIRO’s Data61 Australia

Many practical applications impose a new challenge of utilizing instance-level background knowledge (e.g., subsets of similar or dissimilar data points) within their input data to improve clustering results. In this work, we build on the widely adopted k-center clustering, modeling its input instance-level background knowledge as must-link and cannot-link constraint sets, and formulate the constrained k-center problem. Given the long-standing challenge of developing efficient algorithms for constrained clustering problems, we first derive an efficient approximation algorithm for constrained k-center at the best possible approximation ratio of 2 with LP-rounding technology. Recognizing the limitations of LP-rounding algorithms including high runtime complexity and challenges in parallelization, we subsequently develop a greedy algorithm that does not rely on the LP and can be efficiently parallelized. This algorithm also achieves the same approximation ratio 2 but with lower runtime complexity. Lastly, we empirically evaluate our approximation algorithm against baselines on various real datasets, validating our theoretical findings and demonstrating significant advantages of our algorithm in terms of clustering cost, quality, and runtime complexity. Copyright © 2024, The Authors. All rights reserved.

关键词： Approximation algorithms

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Evaluation of Wireless Sensor Networks Module using IoT Approach

Evaluation of Wireless Sensor Networks Module using IoT Appr...

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Artificial Intelligence and Smart systems (ICAIS), International Conference on

作者： L. Anand Padmalal S J. Seetha R. Juliana PS Naveen Kumar Gayatri Parasa Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattanguluthur Department of Computer Science and Engineering Mangalam College of Engg Kerala Department of Computer Science and Business Systems Panimalar Engineering College Chennai Department of Information Technology Loyola-ICAM College of Engineering and Technology Chennai India Department of CSE-DATA SCIENCE St. Ann's College Of Engineering And Technology Chirala AP Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India

Microcomputers and medical devices with signal transceivers that operate on a specific radio display constitute the backbone of wireless sensor networks (WS Ns) that monitor environmental conditions (temperature, pressure, light, vibration levels, location). It is widely used in WAN sensor networks because of its flexible design and low setup fees. The u200b touch network allows for the connection of up to 65,000 devices, while the Intelligent sensors on other wireless networks are used to transfer data ports and assign wireless networks. Since the price of wireless solutions has been decreasing, and their functional capabilities have been growing, they are gradually replacing wired ones in telemetry data gathering systems and long- distance detecting communication. A deep learning model was used in this investigation to prevent the sensor nodes from manipulating data. Sensor nodes include a lot of parameters and estimations. If these projected data values are altered, network performance will suffer, and the node's lifetime will be reduced. data security became a priority when the sensor nodes were distributed. This new method is 98.82% more efficient than the previous one.

关键词： Temperature sensors Wide area networks Vibrations Temperature measurement Wireless sensor networks Wireless networks Transceivers

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Weighted Sparse Partial Least Squares for Joint Sample and Feature Selection

arXiv

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arXiv 2023年

作者： Min, Wenwen Xu, Taosheng Ding, Chris The Yunnan Key Laboratory of Intelligent Systems and Computing School of Information Science and Engineering Yunnan University Yunnan Kunming650091 China The Institute of Intelligent Machines Hefei Institutes of Physical Science Chinese Academy of Sciences Anhui Hefei230031 China The School of Data Science The Chinese University of Hong Kong Shenzhen China

Sparse Partial Least Squares (sPLS) is a common dimensionality reduction technique for data fusion, which projects data samples from two views by seeking linear combinations with a small number of variables with the maximum variance. However, sPLS extracts the combinations between two data sets with all data samples so that it cannot detect latent subsets of samples. To extend the application of sPLS by identifying a specific subset of samples and remove outliers, we propose an ∞/0-norm constrained weighted sparse PLS (∞/0-wsPLS) method for joint sample and feature selection, where the ∞/0-norm constrains are used to select a subset of samples. We prove that the ∞/0-norm constrains have the Kurdyka-Lojasiewicz property so that a globally convergent algorithm is developed to solve it. Moreover, multi-view data with a same set of samples can be available in various real problems. To this end, we extend the ∞/0-wsPLS model and propose two multi-view wsPLS models for multi-view data fusion. We develop an efficient iterative algorithm for each multi-view wsPLS model and show its convergence property. As well as numerical and biomedical data experiments demonstrate the efficiency of the proposed methods. © 2023, CC BY.

关键词： data fusion

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Emergency Scheduling of Aerial Vehicles via Graph Neural Neighborhood Search

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Guo, Tong Mei, Yi Du, Wenbo Lv, Yisheng Li, Yumeng Song, Tao Beihang University State Key Laboratory of CNS/ATM School of Electronic and Information Engineering Beijing100191 China Victoria University of Wellington Centre for Data Science and Artificial Intelligence & School of Engineering and Computer Science Wellington New Zealand Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China Jinan250353 China University of the Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Beijing Institute of Technology School of Aerospace Engineering Beijing China

The thriving advances in autonomous vehicles and aviation have enabled the efficient implementation of aerial last-mile delivery services to meet the pressing demand for urgent relief supply distribution. Variable Neighborhood Search (VNS) is a promising technique for aerial emergency scheduling. However, the existing VNS methods usually exhaustively explore all considered neighborhoods with a prefixed order, leading to an inefficient search process and slow convergence speed. To address this issue, this paper proposes a novel graph neural neighborhood search algorithm, which includes an online reinforcement learning (RL) agent that guides the search process by selecting the most appropriate low-level local search operators based on the search state. We develop a dual-graph neural representation learning method to extract comprehensive and informative feature representations from the search state. Besides, we propose a reward-shaping policy learning method to address the decaying reward issue along the search process. Extensive experiments conducted across various benchmark instances demonstrate that the proposed algorithm significantly outperforms the state-of-the-art approaches. Further investigations validate the effectiveness of the newly designed knowledge guidance scheme and the learned feature representations. © 2020 IEEE.

关键词： Reinforcement learning

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Temporal patterns selection for All-Cause Mortality prediction in T2D with ANNs

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Journal of Biomedical Informatics 2022年 134卷 104198页

作者： Novitski, Pavel Cohen, Cheli Melzer Karasik, Avraham Hodik, Gabriel Moskovitch, Robert Software and Information Systems Engineering Ben Gurion University Beer-Sheva Israel Maccabi Data Science Institute Maccabi Healthcare Services Tel-Aviv Israel Population Health and Science Ichan Medical School at Mount Sinai NYC United States

Mortality prevention in T2D elderly population having Chronic Kidney Disease (CKD) may be possible thorough risk assessment and predictive modeling. In this study we investigate the ability to predict mortality using heterogeneous Electronic Health Records data. Temporal abstraction is employed to transform the heterogeneous multivariate temporal data into a uniform representation of symbolic time intervals, from which then frequent Time Intervals Related Patterns (TIRPs) are discovered. However, in this study a novel representation of the TIRPs is introduced, which enables to incorporate them in Deep Learning Networks. We describe here the use of iTirps and bTirps, in which the TIRPs are represented by a integer and binary vector representing the time respectively. While bTirp represents whether a TIRP's instance was present, iTirp represents whether multiple instances were present. While the framework showed encouraging results, a major challenge is often the large number of TIRPs, which may cause the models to under-perform. We introduce a novel method for TIRPs’ selection method, called TIRP Ranking Criteria (TRC), which is consists on the TIRP's metrics, such as the differences in its recurrences, its frequencies, and the average duration difference between the classes. Additionally, we introduce an advanced version, called TRC Redundant TIRP Removal (TRC-RTR), TIRPs that highly correlate are candidates for removal. Then the selected subset of iTirp/bTirps is fed into a Deep Learning architecture like a Recurrent Neural Network or a Convolutional Neural Network. Furthermore, a predictive committee is utilized in which raw data and iTirp data are both used as input. Our results show that iTirps-based models that use a subset of iTirps based on the TRC-RTR method outperform models that use raw data or models that use full set of discovered iTirps. © 2022 Elsevier Inc.

关键词： Recurrent neural networks

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E-commerce customer churn prevention using machine learning-based business intelligence strategy

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Measurement: Sensors 2023年 27卷

作者： J, Shobana Gangadhar, Ch. Arora, Rakesh Kumar Renjith, P.N. Bamini, J. Chincholkar, Yugendra devidas Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Electronics & Communication Engineering Prasad V. Potluri Siddhartha Institute of Technology Andhra Pradesh Vijayawada520007 India Department of Information Technology Krishna Engineering College Mohan Nagar UP Ghaziabad India Computer Science and Engineering Department Vellore Institute of Technology Chennai India Sri Ramakrishna College of Arts & Science Tamil Nadu Coimbatore India Department of Electronic & Telecommunication Sinhgad College of Engineering Pune India

Businesses in the E-Commerce sector, especially those in the business-to-consumer segment, are engaged in fierce competition for survival, trying to gain access to their rivals' client bases while keeping current customers from defecting. The cost of acquiring new customers is rising as more competitors join the market with significant upfront expenditures and cutting-edge penetration strategies, making client retention essential for these organizations. The best course of action in this circumstance is to detect prospective churning customers and prevent churn with temporary retention measures. It's also essential to understand why the customer decided to go away to apply customized win-back strategies. Each customer's information, including searches made, purchases made, frequency of purchases, reviews left, feedback is given, and other data, is kept on file by the e-commerce company. Machine learning and data mining may be aided by examining this enormous quantity of data, analysing customer behaviour, and seeing potential attrition opportunities. The support vector machine is a popular supervised learning method in machine learning applications. Predictive analysis uses the hybrid classification approach to address the regression and classification issues. The process for forecasting E-Commerce customer attrition based on support vector machines is presented in this paper, along with a hybrid recommendation strategy for targeted retention initiatives. You may prevent future customer churn by suggesting reasonable offers or services. The empirical findings demonstrate a considerable increase in the coverage ratio, hit ratio, lift degree, precision rate, and other metrics using the integrated forecasting model. To effectively identify separate groups of lost customers and create a customer churn retention strategy, categorize the various lost customer types using the RFM principle. © 2023 The Authors

关键词： Support vector machines

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Intelligent Gen: An Efficient On-Demand Access Protocol in MANETs using Network Simulator - 2

Intelligent Gen: An Efficient On-Demand Access Protocol in M...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： T. Ganesan N. Murali S. Palani Murugan M. Chandrakumar Peter P. Anand Raj S. Manikandan Department of Computer Science & Engineering E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India Department of Artificial Intelligence & Data Science E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India Department of Software Engineering Periyar Maniammai Institute of Science & Technology Thanjavur Tamil Nadu India Department of Computer Science and Business Systems E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India Department of Information Technology E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India

Mobile Ad-Hoc network is a self-configuring and multi-hop collection of mobile devices for making successful communication. The wireless and mobile communication process are encountered based on bandwidth, process control and energy conception. Energy conception is considered and dynamically changed to rely on life time of batteries and power. The life time of battery get exhausted very quickly the nodes are moved and changed rapidly across the network. This paper noticed the problem of life time of power, batteries and energy consumption problem in various routing stages. The proposed Intelligent Gen Algorithm is used to get optimal solution for ensuring high robustness, flexibility and Energy consumption factor. It presents a common framework to balance various routing requirements in MANETs dynamically. The following are the main contributions includes, i. introducing Intelligent GEN Algorithms to classify the throughout bandwidth, delay, data ratio. ii. Various routing performance are evaluated and viewed by speed, packetize and simulation time. The comparison evaluated by select routing algorithm using NS-2. The simulation results are recorded and the performance is monitored by using on demand access protocl. The result clearly demonstrated the proposed Intelligent Gen algorithm used in self-organized manner by wireless channel.

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SIDVis: Designing Visual Interactive System for Analyzing Suicide Ideation Detection

SIDVis: Designing Visual Interactive System for Analyzing Su...

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International Conference on information Visualisation (IV)

作者： Md Rafiqul Islam Md. Kowsar Hossain Sakib Anwaar Ulhaq Shanjita Akter Jianlong Zhou David Asirvathamt Business Information Systems Australian Institute of Higher Education Sydney Australia School of Computing Mathematics and Engineering Charles Sturt University Australia Manna Institute NSW Australia School of Computer Science Taylors University Malaysia Data Science Institute (DSI) University of Technology Sydney NSW Australia

Suicide is a critical global issue that demands a comprehensive examination of factors such as mental illness, substance abuse, financial stress, and trauma. Effectively identifying individuals at risk is vital for intervention and prevention efforts. However, distinguishing suicidal ideation (SID) from non-suicidal language poses challenges. Existing research has addressed this issue, but limited attention has been given to visually interpretable and interactive systems tailored for SID. This study contributes to responsible AI by leveraging deep learning and machine learning techniques to enhance SID detection, enabling proactive interventions and support. In this paper, we introduce SIDVis, an interactive visualization system that improves performance and interpretability at the same time. The rigorous evaluation demonstrates that SIDVis not only outperforms existing methods in terms of accuracy but also provides an explanation for the responsible use of the underlying AI approach, demonstrating its potential to improve SID detection and intervention strategies.

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