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Language engineering (ESOLEC), International Conference on

作者： Maie Aboghazalah Rasha Elnemr Nedaa Elsayed Ayman El-sayed Passant El-Kafrawy Math and Computer Science Department Faculty of Science Menoufia University Egypt Climate change information center and expert systems Agriculture Research Center Giza Egypt Cardiology Department Faculty of Medicine Tanta University Egypt Computer Science and Engineering Department Faculty of Electronic Engineering Menoufia University Egypt Faculty of Science Menoufia University Egypt Effat Collage of Engineering Jeddah SA

ISBN: (纸本)9781665453233

Recommendation systems are now used in a wide range in many fields. In the medical field, recommendation systems have a great stature to both doctors and patients for its accurate prediction. It can reduce the time and efforts spent by doctors and patients. The present work introduces a simple and effective methodology for medical recommendation system based on fuzzy logic. Fuzzy logic is an important method to be used based on fuzzy input data. The input data for each patient are not the same, on which recommendation can differ. This work aims to develop techniques for handling the patient data to urge accurate lifestyle recommendations to the patient. Fuzzy logic is utilized to form different recommendations for the patient like lifestyle recommendations, medicine recommendations, and sports recommendations based on different patient factors like age, gender and patient diseases. After evaluating the system its efficiency reached 94%. This Experiment is the final module in a four modules recommendation system. The first one is responsible for diagnosing chest diseases using ECG signals. The second one makes diagnosis using X-ray images. The third is utilizing the security of the whole system through encryption when sending user data over the cloud.

关键词： Fuzzy logic Medical services Electrocardiography Encryption Security Recommender systems Medical diagnostic imaging

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Experimental observation of ballistic to diffusive transition in AlN thin films

arXiv

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arXiv 2024年

作者： Hoque, Md Shafkat Bin Liao, Michael E. Zare, Saman Liu, Zeyu Koh, Yee Rui Huynh, Kenny Shi, Jingjing Graham, Samuel Luo, Tengfei Ahmad, Habib Doolittle, W. Alan Goorsky, Mark S. Hopkins, Patrick E. Department of Mechanical and Aerospace Engineering University of Virginia CharlottesvilleVA22904 United States Department of Materials Science and Engineering University of California Los AngelesCA90095 United States Department of Applied Physics Hunan University Changsha410300 China George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology AtlantaGA30332 United States School of Materials Science and Engineering Georgia Institute of Technology AtlantaGA30332 United States Department of Aerospace and Mechanical Engineering University of Notre Dame Notre DameIN46556 United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States Department of Materials Science and Engineering University of Virginia CharlottesvilleVA22904 United States Department of Physics University of Virginia CharlottesvilleVA22904 United States

Bulk AlN possesses high thermal conductivity due to long phonon mean-free-paths, high group velocity, and long lifetimes. However, the thermal transport scenario becomes very different in a thin AlN film due to phonon-defect and phonon-boundary scattering. Herein, we report experimental observation of ballistic to diffusive transition in a series of AlN thin films (1.6 - 2440 nm) grown on sapphire substrates. The ballistic transport is characterized by constant thermal resistance as a function of film thickness due to phonon scattering by defects and boundaries. In this transport regime, phonons possess very small group velocities and lifetimes. The lifetime of the optical phonons increases by more than an order of magnitude in the diffusive regime, however, remains nearly constant afterwards. Our study is important for understanding the details of nano and microscale thermal transport in a highly conductive material. Copyright © 2024, The Authors. All rights reserved.

关键词： Sapphire

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Differentially Private Generalized Linear Models Revisited

arXiv

引用

arXiv 2022年

作者： Arora, Raman Bassily, Raef Guzmán, Cristóbal Menart, Michael Ullah, Enayat Department of Computer Science The Johns Hopkins University United States The Ohio State University United States Department of Applied Mathematics University of Twente Institute for Mathematical and Computational Engineering Pontificia Universidad Católica de Chile Chile Department of Computer Science & Engineering The Ohio State University United States

We study the problem of (ϵ, δ)-differentially private learning of linear predictors with convex losses. We provide results for two subclasses of loss functions. The first case is when the loss is smooth and non-negative but not necessarily Lipschitz (such as the squared loss). For this case, we establish an upper bound on the excess population risk of e O (kw*√nk + minn w*2 (nϵ)2/3 , √dw*2 nϵ }), where n is the number of samples, d is the dimension of the problem, and w*is the minimizer of the population risk. Apart from the dependence on w*, our bound is essentially tight in all parameters. In particular, we show a lower bound of Ω(1 √n + minn{w*4/3 (nϵ)2/3 , √dw* nϵ }). We also revisit the previously studied case of Lipschitz losses [SSTT20]. For this case, we close the gap in the existing work and show that the optimal rate is (up to log factors) Θ({kw*√nk + min nkw*√nϵk , √rankw* nϵ }), where rank is the rank of the design matrix. This im- proves over existing work in the high privacy regime. Finally, our algorithms involve a private model selection approach that we develop to enable attaining the stated rates without a-priori knowledge of w*. © 2022, CC BY.

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Continuous and Incremental Learning in physical Human-Robot Cooperation using Probabilistic Movement Primitives

Continuous and Incremental Learning in physical Human-Robot ...

引用

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Daniel Schäle Martin F. Stoelen Erik Kyrkjebø Department of Computer Science Electrical Engineering and Mathematical Sciences Faculty of Engineering and Science Western Norway University of Applied Sciences Førde Norway CRNS University of Plymouth Plymouth United Kingdom

ISBN: (数字)9781728188591

ISBN: (纸本)9781665406802

For a successful deployment of physical Human-Robot Cooperation (pHRC), humans need to be able to teach robots new motor skills quickly. Probabilistic movement primitives (ProMPs) are a promising method to encode a robot’s motor skills learned from human demonstrations in pHRC settings. However, most algorithms to learn ProMPs from human demonstrations operate in batch mode, which is not ideal in pHRC when we want humans and robots to work together from even the first demonstration. In this paper, we propose a new learning algorithm to learn ProMPs incre-mentally and continuously in pHRC settings. Our algorithm incorporates new demonstrations sequentially as they arrive, allowing humans to observe the robot’s learning progress and incrementally shape the robot’s motor skill. A built-in forgetting factor allows for corrective demonstrations resulting from the human’s learning curve or changes in task constraints. We compare the performance of our algorithm to existing batch ProMP algorithms on reference data generated from a pick-and-place task at our lab. Furthermore, we demonstrate how the forgetting factor allows us to adapt to changes in the task. The incremental learning algorithm presented in this paper has the potential to lead to a more intuitive learning progress and to establish a successful cooperation between human and robot faster than training in batch mode.

关键词： Training Shape Probabilistic logic Task analysis Robots

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Machine learning for Autopsy reports Forensic using Text Classification Techniques 2

Machine learning for Autopsy reports Forensic using Text Cla...

引用

2nd International Conference on Computing and Information Technology, ICCIT 2022

作者： Shahid, Muhammad Rehman Munir, Asim Ifraheem, Layeba Aldabbas, Hamza Wadood, Abdul Alwada'n, Tariq University of Faisalabad Departmental of Computational Sciences Faislabad Pakistan International Islamic University Department of Computer Science and Software Engineering Islamabad Pakistan Al-Balqa Applied University Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology Al-Salt Jordan School of Computing Engineering Digital Technologies Teesside University Middlesbrough United Kingdom

ISBN: (纸本)9781665436052

A forensic autopsy is a surgical process in which experts collect a deceased body's internal and external information. These death certificates are the source of timely warnings of an increase in disease activity. It's only helpful if accurate and quantitative data is available. Therefore, the Classification of plain text medical autopsy reports reduces the time consumption and irregularities. The motive is to design an automatic text classification system that classifies plain text autopsy reports. Therefore, a methodology proposes using different Automatic Text Classification Techniques (ATC). This technique has embedded Feature Extraction, Feature Representation, and Feature Reduction techniques. These techniques use for the construction of classification models that classify the text of autopsy reports. Data sets collected from these types will be helpful in future experiments. Finally, the performance of the classifier measures by using different Evaluation parameters. These Evaluation Measures are Precision, Recall, Accuracy, and F-measure. © 2022 IEEE.

关键词： Support vector machines

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Epitaxial lattice-matched Al0.89Sc0.11N/GaN distributed Bragg reflectors

arXiv

引用

arXiv 2023年

作者： van Deurzen, Len Nguyen, Thai-Son Casamento, Joseph Xing, Huili Grace Jena, Debdeep School of Applied and Engineering Physics Cornell University IthacaNY14853 United States Department of Materials Science and Engineering Cornell University IthacaNY14853 United States Department of Electrical and Computer Engineering Cornell University IthacaNY14853 United States Kavli Institute Cornell for Nanoscale Science Cornell University IthacaNY14853 United States

We demonstrate epitaxial lattice-matched Al0.89Sc0.11N/GaN ten and twenty period distributed Bragg reflectors (DBRs) grown on c-plane bulk n-type GaN substrates by plasma-enhanced molecular beam epitaxy (PA-MBE). Resulting from a rapid increase of in-plane lattice coefficient as scandium is incorporated into AlScN, we measure a lattice-matched condition to c-plane GaN for a Sc content of just 11%, resulting in a large refractive index mismatch Δn greater than 0.3 corresponding to an index contrast of Δn/nGaN = 0.12 with GaN. The DBRs demonstrated here are designed for a peak reflectivity at a wavelength of 400 nm reaching a reflectivity of 0.98 for twenty periods. It is highlighted that AlScN/GaN multilayers require fewer periods for a desired reflectivity than other lattice-matched Bragg reflectors such as those based on AlInN/GaN multilayers. © 2023, CC BY-NC-ND.

关键词： Gallium nitride

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A Novel Machine Learning Based Framework for Bridge Condition Analysis

A Novel Machine Learning Based Framework for Bridge Conditio...

引用

IEEE International Conference on Big Data

作者： Mohammad Masum Nafisa Anjum Md Jobair Hossain Faruk Hossain Shahriar Nazmus Sakib Maria Valero M.A. Karim Akond Rahman Fan Wu Alfredo Cuzzocrea Department of Applied Data Science San Jose State University San Jose USA Department of Information Technology Kennesaw State University USA Department of Software Engineering and Game Development Kennesaw State University USA Department of Civil and Environmental Engineering Kennesaw State University USA Department of Computer Science and Software Engineering Auburn University USA Department of Computer Science Tuskegee University USA iDEA Lab University of Calabria Rende Italy

ISBN: (纸本)9781665480468

Bridges play a vital part in the transportation system by ensuring the connectedness of transportation systems, which is critical for a country’s social and economic prosperity by offering daily mobility to the people. However, according to the American Society of Civil Engineers (ASCE 2017), many U.S. bridges are in critical condition, raising safety issues, with 9.1 and 13.6 percent of the country’s 614,387 bridges, respectively, structurally defective, and functionally obsolete. Every day, 178 million people traverse these structurally defective bridges. Furthermore, the average annual failure rate is expected to be between 87 and 222. Bridge breakdowns have disastrous repercussions, and in many cases, result in death. While bridge authorities strive to improve bridge conditions, budget limits make it difficult to make cost-effective maintenance decisions. Bridge authorities distribute limited repair resources based on projected future bridge conditions. As a result, building a data-driven, autonomous, and effective bridge condition prediction model is critical for improving maintenance decision-making. In this paper, we present a novel bridge condition prediction framework using advanced Machine Learning (ML) algorithms on the National Bridge Inventory (NBI) dataset. The framework consists of two stages, where the most informative features from the NBI dataset are selected using the Recursive Feature Elimination process and in the 2 nd step, ML classifiers are applied to the selected features for bridge condition prediction. The experimental results show that the proposed framework can effectively predict bridge conditions by producing highly accurate results in terms of accuracy, precision, recall, and f1-score.

关键词： Bridges Decision making Transportation Machine learning Maintenance engineering Predictive models Big Data

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Routing Single Photons from a Trapped Ion Using a Photonic Integrated Circuit

引用

Physical Review applied 2023年第3期19卷 034001-034001页

作者： Uday Saha James D. Siverns John Hannegan Mihika Prabhu Qudsia Quraishi Dirk Englund Edo Waks Department of Electrical and Computer Engineering University of Maryland College Park Maryland 20742 USA Institute for Research in Electronics and Applied Physics (IREAP) University of Maryland College Park Maryland 20742 USA Department of Physics University of Maryland College Park Maryland 20742 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA United States Army Research Laboratory Adelphi Maryland 20783 USA Joint Quantum Institute (JQI) University of Maryland College Park Maryland 20742 USA

Trapped ions are promising candidates for nodes of a scalable quantum network due to their long-lived qubit coherence times and high-fidelity single- and two-qubit gates. Future quantum networks based on trapped ions will require a scalable way to route photons between different nodes. Photonic integrated circuits from fabrication foundries provide a compact solution to this problem. However, these circuits typically operate at telecommunication wavelengths that are incompatible with the strong dipole emissions of trapped ions. In this work, we demonstrate the routing of single photons from a trapped ion using a photonic integrated circuit. We employ quantum frequency conversion to match the emission of the ion to the operating wavelength of a foundry-fabricated silicon nitride photonic integrated circuit, achieving a total transmission of 31.0% ± 0.9% through the device. Using programmable phase shifters, we switch the single photons between the output channels of the circuit and demonstrate a 50:50 beam splitting condition. These results constitute an important step towards programmable routing and entanglement distribution in large-scale quantum networks and distributed quantum computers.

关键词： Integrated optics Optical quantum information processing Quantum communication Quantum information with trapped ions Quantum interconnects Quantum networks Trapped ions

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Arabic Documents Clustering using Bond Energy Algorithm and Genetic Algorithm

Arabic Documents Clustering using Bond Energy Algorithm and ...

引用

International Conference on Information and Communication Systems (ICICS)

作者： Hadeel Alazzam Orieb AbuAlghanam Abdulsalam Alsmady Esra’ a Alhenawi Department of Intelligence Systems Al-Balqa Applied University Al-Salt Jordan Networks and Information Security Alahliyya Amman University Amman Jordan Department of Computer Engineering Jordan University of Science and Technology Irbid Jordan Department of Computer Science University of Jordan Amman Jordan

Text clustering is a popular data mining process used in data indexing and information retrieval. However, Existing clustering techniques suffer from several shortcomings such as sensitivity to the initial value, slow convergence, etc... In this paper, the Bond Energy Algorithm (BEA) has been used for clustering Arabic documents. The bond energy algorithm has been modified with the help of a Genetic algorithm to enhance the accuracy of the results. The efficiency of modified BEA has been compared to BEA baseline and K-means clustering in terms of precision, recall, and F-Score. The results show that Modified BEA can be used for clustering Arabic Documents, and the efficiency of the algorithm can be enhanced in the future to give a better result.

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Updating high average-utility itemsets with pre-large concept

Updating high average-utility itemsets with pre-large concep...

引用

作者： Wu, Jimmy Ming-Tai Teng, Qian Lin, Jerry Chun-Wei Yun, Unil Chen, Hsing-Chung College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Department of Computer Engineering Sejong University Seoul Korea Republic of Department of Computer Science and Information Engineering Asia University Taichung Taiwan

HAUIM (High Average-Utility Itemset Mining) is a variation of HUIM (High-Utility Itemset Mining) that provides a reliable measure to reveal utility patterns in light of the length of the mined pattern. Several works have been studied to improve mining efficiency by designing multiple pruning strategies and efficient frameworks, but fewer studies have centered on the sophisticated database maintenance algorithm. Existing works still have to rescan the databases multiple times when it is necessary. We first use the pre-large principle in this paper to efficiently update the newly discovered HAUIs. For further updates and maintenance on the basis of the two thresholds, the Pre-large Average Utility Itemset (PAUI) can be maintained to increase the mining performance. Experiments will then be performed to compare the batch model, the Fast-Updated (FUP)-based model, and the Apriori-like HAUIM (APHAUIM) model designed in respect of the number of maintenance patterns, scalability, runtime, and memory usage. © 2020 - IOS Press and the authors. All rights reserved.

关键词： Maintenance

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