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arXiv 2022年

作者： Joloudari, Javad Hassannataj Mojrian, Sanaz Saadatfar, Hamid Nodehi, Issa Fazl, Fatemeh Shirkharkolaie, Sahar Khanjani Alizadehsani, Roohallah Dipu Kabir, H.M. Tan, Ru-San Rajendra Acharya, U. Department of Computer Engineering Faculty of Engineering University of Birjand Birjand*** Iran Department of Information Technology Mazandaran University of Science and Technology Babol Iran Department of Computer Engineering Qom University of Technology Qom Iran Department of Electronic Engineering Faculty of Electrical and Computer Engineering University of Birjand Birjand Iran Institute for Intelligent Systems Research and Innovation Deakin University GeelongVIC3216 Australia School of Science and Technology Singapore University of Social Sciences Singapore Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan

With the increasing growth of information through smart devices, increasing the quality level of human life requires various computational paradigms presentation including the Internet of Things, fog, and cloud. Between these three paradigms, the cloud computing paradigm as an emerging technology adds cloud layer services to the edge of the network so that resource allocation operations occur close to the end-user to reduce resource processing time and network traffic overhead. Hence, the resource allocation problem for its providers in terms of presenting a suitable platform, by using computational paradigms is considered a challenge. In general, resource allocation approaches are divided into two methods, including auction-based methods (goal, increase profits for service providers-increase user satisfaction and usability) and optimization-based methods (energy, cost, network exploitation, Runtime, reduction of time delay). In this paper, according to the latest scientific achievements, a comprehensive literature study (CLS) on artificial intelligence methods based on resource allocation optimization without considering auction-based methods in various computing environments are provided such as cloud computing, Vehicular Fog Computing, wireless, IoT, vehicular networks, 5G networks, vehicular cloud architecture, machine-to-machine communication (M2M), Train-to-Train(T2T) communication network, Peer-to-Peer (P2P) network. Since deep learning methods based on artificial intelligence are used as the most important methods in resource allocation problems;Therefore, in this paper, resource allocation approaches based on deep learning are also used in the mentioned computational environments such as deep reinforcement learning, Q-learning technique, reinforcement learning, online learning, and also Classical learning methods such as Bayesian learning, Cummins clustering, Markov decision process. © 2022, CC BY-SA.

关键词： Reinforcement learning

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Loss Decomposition and Centroid Estimation for Positive and Unlabeled Learning

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IEEE transactions on pattern analysis and machine intelligence 2021年第3期43卷 918-932页

作者： Chen Gong Hong Shi Tongliang Liu Chuang Zhang Jian Yang Dacheng Tao PCA Lab the Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing P.R. China PCA Lab the Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Laboratory of Image and Video Understanding for Social Security the School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing P.R. China UBTECH Sydney Artificial Intelligence Centre School of Computer Science Faculty of Engineering University of Sydney Darlington NSW Australia

This paper studies Positive and Unlabeled learning (PU learning), of which the target is to build a binary classifier where only positive data and unlabeled data are available for classifier training. To deal with the absence of negative training data, we first regard all unlabeled data as negative examples with false negative labels, and then convert PU learning into the risk minimization problem in the presence of such one-side label noise. Specifically, we propose a novel PU learning algorithm dubbed "Loss Decomposition and Centroid Estimation" (LDCE). By decomposing the loss function of corrupted negative examples into two parts, we show that only the second part is affected by the noisy labels. Thereby, we may estimate the centroid of corrupted negative set via an unbiased way to reduce the adverse impact of such label noise. Furthermore, we propose the "Kernelized LDCE" (KLDCE) by introducing the kernel trick, and show that KLDCE can be easily solved by combining Alternative Convex Search (ACS) and Sequential Minimal Optimization (SMO). Theoretically, we derive the generalization error bound which suggests that the generalization risk of our model converges to the empirical risk with the order of O(1/√k+1/√{n-k}+1/√n) ( n and k are the amounts of training data and positive data correspondingly). Experimentally, we conduct intensive experiments on synthetic dataset, UCI benchmark datasets and real-world datasets, and the results demonstrate that our approaches (LDCE and KLDCE) achieve the top-level performance when compared with both classic and state-of-the-art PU learning methods.

关键词： Learning Artificial Intelligence Minimisation Pattern Classification Positive data KLDCE Loss Decomposition Centroid Estimation Positive Learning Unlabeled Learning Binary Classifier Unlabeled data Classifier Training Negative Training data False Negative Labels Risk Minimization Problem PU Learning Algorithm Loss Function Corrupted Negative Examples Noisy Labels Corrupted Negative Set Generalization Risk Empirical Risk Kernelized LDCE One Side Label Noise Alternative Convex Search ACS Sequential Minimal Optimization SMO Generalization Error Bound Estimation Training Supervised Learning Noise Measurement Analytical Models Risk Management Kernel PU Learning Loss Decomposition Centroid Estimation Kernel Extension Generalization Bound

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Residential Energy Consumption Prediction Using Inter-Household Energy data and Socioeconomic Information

Residential Energy Consumption Prediction Using Inter-Househ...

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European Signal Processing Conference (EUSIPCO)

作者： Pascal A. Schirmer Christian Geiger Iosif Mporas Comm. & Intelligent Systems Group School of Eng. & Computer Science University of Hertfordshire Hatfield UK Institute for Machine Tools Faculty of Mechanical Engineering Technical University of Munich Garching Germany

ISBN: (数字)9789082797053

ISBN: (纸本)9781728150017

Previous studies have shown that residential energy consumption prediction accuracy can be improved when households energy data are fused with residents' socioeconomic information. In this article we propose an architecture for the prediction of residential energy consumption using past energy consumption from other/neighboring households in combination with socioeconomic information of the corresponding residents. The architecture is based on a Long Short Term Memory model and was evaluated using a large-scale dataset monitoring households of London. The proposed approach significantly improves the accuracy of the energy consumption predictor reducing the mean absolute error up to 25.2% with prediction error rate equal to 5.4%.

关键词： Energy consumption Signal processing Predictive models Market research History Monitoring Load modeling

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DieT Transformer model with PCA-ADE integration for advanced multi-class brain tumor classification

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Intelligence-Based Medicine 2025年 11卷 100192-100192页

作者： Amin, Mohammad Nahar, Khalid M.O. Gharaibeh, Hasan Nasayreh, Ahmad Alsalmanc, Neda'a Alomar, Alaa Malkawi, Majd Alqasem, Noor Smerat, Aseel Zitar, Raed Abu Nusier, Shawd Ezugwu, Absalom E. Abualigah, Laith Department of Information Technology and Computer Sciences Yarmouk University Irbid 211633 Jordan Faculty of Computer Studies Arab Open University P.O.Box 84901 Riyadh 11681 Saudi Arabia Department of Computer Science Faculty of Information Technology Jordan University of Science and Technology Irbid 22110 Jordan Department of Medicine Faculty of Medicine Jordan University of Science and Technology Irbid 22110 Jordan Department of Biomedical Systems and Informatics Engineering Hijjawi Faculty for Engineering Technology Yarmouk University Irbid 21163 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk 71491 Saudi Arabia Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates Department of Health and Clinical Outcomes Research Saint Louis University St. Louis MO United States Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom 2520 South Africa Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan

Early and accurate diagnosis of brain tumors is crucial to improving patient outcomes and optimizing treatment strategies. Long-term brain injury results from aberrant proliferation of either malignant or nonmalignant tissues in the brain. MRIs, or magnetic resonance imaging, are one of the most used approaches for detecting brain tumors. Professionals physically evaluate people after they have had MRI filtering, the process of enhancing MRI scans for radiologist interpretation, to establish if they have a brain tumor. Because different specialists use different frames to make judgments on the same MRI image, their analyses may yield contradictory results. Furthermore, simply detecting a tumor is insufficient. Inconsistent diagnoses can lead to delays in treatment, impacting survival rates and quality of care. It is also crucial to diagnose the patient's tumor so that treatment can begin as soon as possible. In this research, we investigate the multi-class classification of brain tumors utilizing a cutting-edge methodology that includes feature extraction from pictures using the DieT Transformer model, dimensionality reduction with PCA, and feature selection using the ADE algorithm. The proposed model, known in the publication as ADE_DieT, obtained an accuracy of 96.09 %. In addition, this article analyzes the performance of various pre-trained models, including MobileNetV3, NasNet, ResNet50, VGG16, VGG19, and DeiT. The proposed approach shortens the time required for manual diagnosis by clinicians by assisting in the rapid and accurate identification of brain tumors using MRI data. In oncology, this is important since it allows for early treatment. Integrating ADE_DieT into clinical workflows can support radiologists by reducing diagnosis time and enhancing diagnostic consistency. © 2024 The Authors

关键词： Brain tumor Medical image analysis MRI Vision Transformer

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Automatically Labeling Aging Scenarios with a Machine Learning Approach 1

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24th International Conference on Human-Computer Interaction, HCII 2022

作者： An, Ning Xu, Yang Gao, Qinglin Zhu, Wenjie Wu, Aoran Chen, Honglin School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China National Smart Eldercare International S&T Cooperation Base Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Shanghai Tianyu Senior Living Service Co. Ltd. Shanghai China School of Basic Courses Bengbu Medical College Bengbu China Management and Business Department Skidmore College Saratoga SpringsNY United States Department of Social Sciences University of Eastern Finland Kuopio70150 Finland

ISBN: (数字)9783031179020

ISBN: (纸本)9783031179013

Older adults have diversified needs often associated with particular aging scenarios. People started to use aging scenarios to provide better Smart Eldercare services and develop innovative solutions. The problem is how to manage these scenarios effectively. To our best knowledge, this study is the first to develop a model to provide a structured framework of aging scenarios for research and teaching of eldercare services. Labeling aging scenarios can facilitate their collection, classification, and consolidation within this model. This study uses the automatic labeling approach to manage and utilize aging scenarios. Under each class, there are labels with different numbers and smaller granularity to reflect the requirements in each scenario case. Since a scenario case often involves more than one label, properly labeling it becomes a multi-label text classification problem. We extend the multi-label text classification model LSAN for labeling aging scenario cases. For evaluation purposes, we collected 938 scenario cases as the dataset. Experimental results show that our proposed method can achieve an average accuracy of 61.66%, better than other classical methods. © 2022, Springer Nature Switzerland AG.

关键词： Classification (of information)

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LEXpander: applying colexification networks to automated lexicon expansion

arXiv

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arXiv 2022年

作者： Di Natale, Anna Garcia, David Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Spitalgasse 23 BT88 Vienna1090 Austria Faculty of Computer Science and Biomedical Engineering Graz University of Technology Inffeldgasse 16c/I Graz8010 Austria Complexity Science Hub Vienna Josefstädter Straße 39 Wien1080 Austria

Background/Introduction Recent approaches to text analysis from social media and other corpora rely on word lists to detect topics, measure meaning, or to select relevant documents. These lists are often generated by applying computational lexicon expansion methods to small, manually-curated sets of root words. Despite the wide use of this approach, we still lack an exhaustive comparative analysis of the performance of lexicon expansion methods and how they can be improved with additional linguistic data. Methods In this work, we present LEXpander, a method for lexicon expansion that leverages novel data on colexification, i.e. semantic networks connecting words based on shared concepts and translations to other languages. We evaluate LEXpander in a benchmark including widely used methods for lexicon expansion based on various word embedding models and synonym networks. Results We find that LEXpander outperforms existing approaches in terms of both precision and the trade-off between precision and recall of generated word lists in a variety of tests. Our benchmark includes several linguistic categories and sentiment variables in English and German. We also show that the expanded word lists constitute a high-performing text analysis method in application cases to various corpora. Conclusion This way, LEXpander poses a systematic automated solution to expand short lists of words into exhaustive and accurate word lists that can closely approximate word lists generated by experts in psychology and linguistics. Copyright © 2022, The Authors. All rights reserved.

关键词： Embeddings

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Retraction Note: Radar optical communication for analysing aerial targets with frequency bandwidth and clutter suppression by boundary element mmwave signal model

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Optical and Quantum Electronics 2024年第12期56卷 1-1页

作者： Kavitha, V. P. Prabakar, D. Ranjith subramanian, S Balambigai, S. Department of ECE SRM Institute of Science and Technology Chennai India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Insitute of Science and Technology Kattankulathur India Department of Electronic and Communication Engineering Jeppiaar Engineering College Chennai India Department of ECE Kongu Engineering College Perundurai India

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One City, Two Tales: Using Mobility Networks to Understand Neighborhood Resilience and Fragility during the COVID-19 Pandemic

arXiv

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arXiv 2022年

作者： Boz, Hasan Alp Bahrami, Mohsen Balcisoy, Selim Bozkaya, Burcin Mazar, Nina Nichols, Aaron Pentland, Alex Sabanci University Faculty of Engineering and Natural Sciences Istanbul34956 Turkey Massachusetts Institute of Technology Institute for Data Systems and Society CambridgeMA02139 United States New College of Florida The Graduate Program in Applied Data Science 5800 Bay Shore Rd SarasotaFL34243 United States Boston University Questrom School of Business BostonMA02215 United States

What predicts a neighborhood’s resilience and adaptability to essential public health policies and shelter-in-place regulations that prevent the harmful spread of COVID-19? To answer this question, in this paper we present a novel application of human mobility patterns and human behavior in a network setting. We analyze mobility data in New York City over two years, from January 2019 to December 2020, and create weekly mobility networks between Census Block Groups by aggregating Point of Interest level visit patterns. Our results suggest that both the socioeconomic and geographic attributes of neighborhoods significantly predict neighborhood adaptability to the shelter-in-place policies active at that time. That is, our findings and simulation results reveal that in addition to factors such as race, education, and income, geographical attributes such as access to amenities in a neighborhood that satisfy community needs were equally important factors for predicting neighborhood adaptability and the spread of COVID-19. The results of our study provide insights that can enhance urban planning strategies that contribute to pandemic alleviation efforts, which in turn may help urban areas become more resilient to exogenous shocks such as the COVID-19 pandemic. © 2022, CC BY.

关键词： COVID-19

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A generalized machine learning model for long-term coral reef monitoring in the Red Sea

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Heliyon 2024年第18期10卷 e38249页

作者： Gapper, Justin J. Maharjan, Surendra Li, Wenzhao Linstead, Erik Tiwari, Surya P. Qurban, Mohamed A. El-Askary, Hesham Earth Systems Science and Data Solutions Lab Chapman University Orange 92866 CA United States Schmid College of Science and Technology Chapman University Orange 92866 CA United States Fowler School of Engineering Chapman University Orange 92866 CA United States Center for Environment and Water The Research Institute King Fahd University of Petroleum and Minerals (KFUPM) Dhahran 31261 Saudi Arabia National Center for Wildlife Saudi Arabia Department of Environmental Sciences Faculty of Science Alexandria University Moharem Bek Alexandria 21522 Egypt

Coral reefs, despite covering less than 0.2 % of the ocean floor, harbor approximately 35 % of all known marine species, making their conservation critical. However, coral bleaching, exacerbated by climate change and phenomena such as El Niño, poses a significant threat to these ecosystems. This study focuses on the Red Sea, proposing a generalized machine learning approach to detect and monitor changes in coral reef cover over an 18-year period (2000–2018). Using Landsat 7 and 8 data, a Support Vector Machine (SVM) classifier was trained on depth-invariant indices (DII) derived from the Gulf of Aqaba and validated against ground truth data from Umluj. The classifier was then applied to Al Wajh, demonstrating its robustness across different sites and times. Results indicated a significant decline in coral cover: 11.4 % in the Gulf of Aqaba, 3.4 % in Umluj, and 13.6 % in Al Wajh. This study highlights the importance of continuous monitoring using generalized classifiers to mitigate the impacts of environmental changes on coral reefs. © 2024 The Authors

关键词： Classification Landsat Machine learning Spatiotemporal coral change analysis SVM

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Probabilistic Robust Linear Quadratic Regulators with Gaussian Processes

arXiv

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arXiv 2021年

作者： von Rohr, Alexander Neumann-Brosig, Matthias Trimpe, Sebastian Institut for Data Science in Mechanical Engineering RWTH Aachen University Germany Institut Analysis und Algebra TU Braunschweig Germany Stuttgart Tübingen Germany Max Planck Institute for Intelligent Systems Stuttgart Tübingen Germany

Probabilistic models such as Gaussian processes (GPs) are powerful tools to learn unknown dynamical systems from data for subsequent use in control design. While learning-based control has the potential to yield superior performance in demanding applications, robustness to uncertainty remains an important challenge. Since Bayesian methods quantify uncertainty of the learning results, it is natural to incorporate these uncertainties into a robust design. In contrast to most state-of-the-art approaches that consider worst-case estimates, we leverage the learning method's posterior distribution in the controller synthesis. The result is a more informed and, thus, more efficient tradeoff between performance and robustness. We present a novel controller synthesis for linearized GP dynamics that yields robust controllers with respect to a probabilistic stability margin. The formulation is based on a recently proposed algorithm for linear quadratic control synthesis, which we extend by giving probabilistic robustness guarantees in the form of credibility bounds for the system's stability. Comparisons to existing methods based on worst-case and certainty-equivalence designs reveal superior performance and robustness properties of the proposed method. Copyright © 2021, The Authors. All rights reserved.

关键词： Robust control

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