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SSRN 2023年

作者： Healy, Laura Zhu, Xianglu Dong, Gaoya Selli, Serkan Kelebek, Hasim Sullivan, Carl Tiwari, Uma Tiwari, Brijesh K. Teagasc Food Research Centre Ashtown Dublin Ireland Department of Food Science and Environmental Health Technological University Dublin Dublin Ireland Technological University Dublin Ireland Department of Food Engineering Faculty of Agriculture Cukurova University Adana01330 Turkey Department of Nutrition and Dietetics Faculty of Health Sciences Cukurova University Adana01330 Turkey Department of Food Engineering Faculty of Engineering Adana AlparslanTurkes Science and Technology University Adana Turkey Faculty of Computing Digital and Data School of Mathematics and Statistics Technological University Dublin Dublin Ireland

An investigation into the impact of novel pretreatments on fermentation indicators of seaweed was carried out. High pressure processing (HPP), ultrasound probe (USP) and ultrasound bath (USB) technologies were employed as a pretreatment ahead of fermentation of brown seaweed species Alaria esculenta by Lactiplantibacillus plantarum (LAB) and a symbiotic culture of bacteria and yeast (SCOBY). pH was significantly different (p 0.05). Organic acids differed significantly (p © 2023, The Authors. All rights reserved.

关键词： Fermentation

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Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes

Classification of Breast Thermal Images into Healthy/Cancer ...

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2022 International Conference on Machine Learning and data Engineering, ICMLDE 2022

作者： Kadry, Seifedine Crespo, Rubén González Herrera-Viedma, Enrique Krishnamoorthy, Sujatha Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja Andalusia26006 Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai602105 India

In the women's community, Breast Cancer (BC) is a severe disease. The World Health Organization reported in 2020 that 2.26 million deaths occur due to BC. BC is curable if detected early. Since thermal imaging is non-invasive and supports disease detection, it is commonly used in clinics. Compared to other methods, it keeps BC early and accurate. The proposed work aims to evaluate the performance of the Pretrained Deep-Learning Methods (PDLM) in detecting BC using the thermal images collected from the benchmark dataset. It includes the following stages: primary image processing, deep feature mining, handcrafted feature mining, feature optimization using Firefly-Algorithm (FA), classification and validation. Visual Lab thermal images were used in the study. The investigational outcome of this study authenticates that the VGG16, along with the DT, provides better detection accuracy (95.5%) compared to other classifiers used in this study. To justify the significance of the implemented technique, the proposed work not only improved accuracy, but also improved precision, sensitivity, specificity, and F1-Scores. © 2023 The Authors. Published by Elsevier B.V.

关键词： Classification (of information)

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Machine learning-based classification of medication adherence among patients with noncommunicable diseases

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Informatics in Medicine Unlocked 2025年 52卷

作者： Kanyongo, Wellington Ezugwu, Absalom E. Moyo, Tsitsi Fonou Dombeu, Jean Vincent Department of Computer Science Faculty of Science Engineering Bindura University of Science Education Bindura Zimbabwe Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom 2520 South Africa Cimas Medical Aid Society Borrowdale Office Park Borrowdale Road P. O Box 1243 Harare Zimbabwe School of Mathematics Statistics and Computer Science University of KwaZulu-Natal Pietermaritzburg Campus Pietermaritzburg 3201 South Africa

Non-adherence to medication among individuals with non-communicable diseases (NCDs) leads to increased morbidity, mortality, and healthcare costs. The integration of electronic drug prescription and dispensation systems enables comprehensive analysis of medication adherence (MA). Patient-level and medical claims data for 8141 diabetic and hypertensive patients in Harare, Zimbabwe, were analysed. Non-adherence was defined as medication refills falling below 75 % of the intended 12 monthly claims, while adherence required at least 75 % of the refills. Classification employed multiple machine learning algorithms, including SVM, KNN, DT, Naïve Bayes, DNN, LR, and RF in Python 3.11.3. Significant variables for MA were identified through the Random Forest (RF) feature importance mechanism and the information gain technique. These included the annual quantity of medical supplies, annual claim amount, patient age, wellness program subscription, medical aid cover, contribution towards medical aid cover, comorbidity, diagnosis, hospital cover type, complications development, gender, and medical aid scheme. The total units of medical supplies dispensed annually emerged as the most significant predictor of MA. Considering the 8-feature subset, which consistently produced the most robust machine learning models, the classification accuracy of the ML classifiers ranged from 84.9 % to 88.2 %, while the AUC values varied between 0.857 and 0.934. RF, an ensemble learning technique, was the most robust classifier, achieving 88.2 % accuracy, an AUC of 0.935, and superior precision, recall, and F1-score. This model shows promise as a prognostic tool for enhancing MA, aiding in identifying adherence levels among patients. These findings contribute to addressing disparities in medication refilling and adherence rates among patients with NCDs. The ML model holds potential for the development of intelligent MA and intervention applications to improve patient adherence to medication in the

关键词： Chronic disease Decision trees Deep neural network Diabetes Hypertension k-NN Logistic regression Machine learning Medication adherence Naïve bayes Non-communicable diseases Random forest SVM

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Multi-Graph Fusion Networks for Urban Region Embedding

arXiv

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arXiv 2022年

作者： Wu, Shangbin Yan, Xu Fan, Xiaoliang Pan, Shirui Zhu, Shichao Zheng, Chuanpan Cheng, Ming Wang, Cheng Fujian Key Laboratory of Sensing and Computing for Smart Cities School of Informatics Department of Computer Science and Technology Xiamen University China Department of Data Science and AI Faculty of Information Technology Monash University Australia University of Chinese Academy of Sciences China

Learning the embeddings for urban regions from human mobility data can reveal the functionality of regions, and then enables the correlated but distinct tasks such as crime prediction. Human mobility data contains rich but abundant information, which yields to the comprehensive region embeddings for cross domain tasks. In this paper, we propose multi-graph fusion networks (MGFN) to enable the cross domain prediction tasks. First, we integrate the graphs with spatio-temporal similarity as mobility patterns through a mobility graph fusion module. Then, in the mobility pattern joint learning module, we design the multi-level cross-attention mechanism to learn the comprehensive embeddings from multiple mobility patterns based on intra-pattern and inter-pattern messages. Finally, we conduct extensive experiments on real-world urban datasets. Experimental results demonstrate that the proposed MGFN outperforms the state-of-the-art methods by up to 13.11% improvement. https://***/wushangbin/MGFN. Copyright © 2022, The Authors. All rights reserved.

关键词： Embeddings

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Hierarchical Reverse Games for the Resource Ecosystem in Cloud-Edge computing

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IEEE Transactions on Consumer Electronics 2024年

作者： Pei, Manlin Wang, Chenyu Wang, Xia Zheng, Zhigao Huang, Jianhui Wang, Shengling Guo, Yufei School of Computer and Information Technology Beijing Jiaotong University Beijing China Department of Computer Science Georgia State University United States Beijing University of Technology School of Statistics and Data Science Faculty of Science Beijing China Institute of Artificial Intelligence School of Computer Science National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Hubei Luojia Laboratory Wuhan University Wuhan China Chinese Academy of Sciences Institute of Computing Technology Beijing China Beijing Normal University School of Artificial Intelligence Beijing China George Washington University Department of Statistics United States

Edge computing has emerged as a killer technology for a hyper-connected world due to its distributed architecture and customer-proximity property. Combined edge nodes with the cloud data center, a cloud-edge computing paradigm is formed, whose resource ecosystem calls for competitive pricing and optimal resource allocation. However, it is not feasible to realize global information-based optimization since some information is kept private to their owners. To solve this challenge, this paper develops the user-independent hierarchical reverse game and the user-assisted hierarchical reverse game to depict the relationship among the resource provider, the resource tenant, and the resource user. The aim is to seek the optimal strategies for players without revealing any private information. The common trait of both games is to force the players to report the optimal strategies based on their actual private information. This salient trait can produce privacy-preservation optimization since there is no requirement for the players to release their individual information directly, and the optimal strategies of other players can be deduced naturally. The difference between the user-independent hierarchical reverse game and the user-assisted one is that the game rules are stipulated entirely by the provider and the tenants (edge nodes) in the former, while some game rules can be customized according to the advice of users in the latter. This leads the user-independent hierarchical reverse game is more concise and easy to guarantee the incentive compatibility constraint, while the user-assisted hierarchical reverse game can attract more users since the game rules can adapt to their economic status, requirements, and preferences. © 1975-2011 IEEE.

关键词： Cloud platforms

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Multimodal Dialogue Emotion Recognition Based on Label Optimization and Coarse-Grained Assisted Fine-Grained

Multimodal Dialogue Emotion Recognition Based on Label Optim...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhibo Zhang Zhenyu Yang Yan Huang Baojie Xu Wenyue Hu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multimodal dialogue emotion recognition integrates data from multiple modalities to accurately identify emotional states in conversations. However, differences in expression and information density across modalities complicate the fusion of features. Traditional methods may introduce redundant information from other utterances, reducing the accuracy of emotion recognition. Existing one-hot labels often fail to capture the full range of emotional expressions, leading to biased results. To address these issues, we propose a model that fuses different modalities within the same utterance to avoid redundancy. It employs a progressive classification process, refining emotion recognition from coarse to fine granularity. Additionally, we use emotion polarity probabilities as weights for fine-grained classification and introduce a multimodal information-rich label that considers both the data and their interactions. Experiments on IEMOCAP and MELD datasets demonstrate the model’s effectiveness, significantly improving dialog emotion recognition accuracy. Our code is available at https://***/r/LOCG-188E.

关键词： Emotion recognition Accuracy Fuses Refining Termination of employment Speech recognition Signal processing Speech processing Optimization Faces

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Cyclic Quantum Teleportation of Two-Qubit Entangled States by using Six-Qubit Cluster State and Six-Qubit Entangled State

arXiv

引用

arXiv 2024年

作者： Slaoui, A. El Kirdi, M. Ahl Laamara, R. Alabdulhafith, M. Chelloug, S.A. Abd El-Latif, A.A. LPHE-Modeling and Simulation Faculty of Sciences Mohammed V University in Rabat Rabat Morocco Centre of Physics and Mathematics CPM Faculty of Sciences Mohammed V University in Rabat Rabat Morocco Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia EIAS Data Science Lab College of Computer and Information Sciences Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom32511 Egypt

Cyclic quantum teleportation schemes requires at least the existence of three collaborators acting all as senders and receivers of quantum information, each one of them has an information to be transmitted to the next neighbour in a circular manner. Here, new cyclic quantum teleportation scheme is proposed for perfectly transmitting cyclically three arbitrary unknown two-qubit states (α, β and γ) among the three collaborators. In this scheme, Alice can send to Bob the quantum information contained in her two-qubit state α and receive from Charlie the quantum information contained in the two-qubit state in his possession γ and similarly, Bob can transmit to Charlie the quantum information contained in his two-qubit state β through a quantum channel of twelve-qubit state consisting of a six-qubit cluster state and a six-qubit entangled state by sequentially and cyclically performing Bell state measurements. Subsequently, each one of the three participants can afterwards retrieve his own desired two-qubit state using classical channel and by performing appropriate unitary Pauli operators and we have shown that our proposed scheme performs efficiently. © 2024, CC0.

关键词： Qubits

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An Efficient Model for Detection of Wood Surface Defects

An Efficient Model for Detection of Wood Surface Defects

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International Conference on Computer Supported Cooperative Work in Design

作者： Fengqi Hao Junjie Xia Haigang Xu Hoiio Kong Qingyan Ding Jinqiang Bai Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Data Science City University of Macau Macau China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Shandong Shifeng (Group) Company Ltd. China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Detection of wood surface defects is one critical step in the plywood process. YOLO series detection models need to find a good tradeoff between detection speed and accuracy. Therefore, this paper improves the YOLOv8 model by replacing the backbone, neck, and head with the FasterNet, Enhanced Feature Pyramid Network (EFPN), and Multi-Scale Feature Decoupling Head (MSDH), respectively. The FasterNet reduces the number of model parameters by using Partial Convolution (PConv). To remedy the accuracy loss caused by the FasterNet, the EFPN adds auxiliary branches to reduce information loss between different feature layers and uses Dilated Reparameter Modules (DRM) to extract more multi-scale features. Besides, the MSDH reorganizes the features on different scales to enhance their semantic relationships. Experimental results show that FasterNet reduces the number of parameters by 43%, the EFPN and the MSDH increase the mAP (mean Average Precison) by 2.2% and 1.4%, respectively, compared to the original Yolov8 model.

关键词： YOLO Accuracy Computational modeling Semantics Feature extraction Neck Rubber Surface treatment Defect detection Sorting

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Classification-based self-learning for weakly supervised bilingual lexicon induction 58

Classification-based self-learning for weakly supervised bil...

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58th Annual Meeting of the Association for Computational Linguistics, ACL 2020

作者： Karan, Mladen Vulić, Ivan Korhonen, Anna Glavaš, Goran TakeLab Faculty of Electrical Engineering and Computing University of Zagreb Language Technology Lab TAL University of Cambridge Data and Web Science Group University of Mannheim

ISBN: (纸本)9781952148255

Effective projection-based cross-lingual word embedding (CLWE) induction critically relies on the iterative self-learning procedure. It gradually expands the initial small seed dictionary to learn improved cross-lingual mappings. In this work, we present CLASSYMAP, a classification-based approach to self-learning, yielding a more robust and a more effective induction of projection-based CLWEs. Unlike prior self-learning methods, our approach allows for integration of diverse features into the iterative process. We show the benefits of CLASSYMAP for bilingual lexicon induction: we report consistent improvements in a weakly supervised setup (500 seed translation pairs) on a benchmark with 28 language pairs. © 2020 Association for Computational Linguistics

关键词： Iterative methods

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Women's Health Diary: Tracker System for Menstruation, Intermenstrual, Ovulation, and Postnatal Bleeding Using Rules-Based Technique

Women's Health Diary: Tracker System for Menstruation, Inter...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Nursyuhada binti Abdullah Azwa bin Abdul Aziz Wan Azani Mustafa Muntather Almusawi Abbas Hameed Abdul Hussein Faculty of Informatics & Computing Universiti Sultan Zainal Abidin (UniSZA) Besut Campus Terengganu Malaysia Data Science & Analytics (DASA) Special Interest Group (SIG) Universiti Sultan Zainal Abidin (UniSZA) Terengganu Malaysia Centre of Excellence Advanced Computing (CoE AdvComp) Universiti Malaysia Perlis Arau Perlis Malaysia Faculty of Engineering The Islamic University Najaf Iraq College of Pharmacy Ahl Al Bayt University Karbala Iraq

Menstruation, a natural physiological process in women, entails regular vaginal bleeding as part of the monthly menstrual cycle. While existing systems facilitate menstrual cycle tracking, they often lack the capability to differentiate between various types of vaginal bleeding, such as menstruation (haid), intermenstrual bleeding (istihadah), or postnatal bleeding (nifas). To address this limitation, the Women's Health Diary: Tracker System for Menstruation, Intermenstrual, Ovulation, and Postnatal Bleeding) was developed using a rule-based technique. This system employs an iterative method comprising eight phases: initial planning, planning, requirements, analysis and design, implementation, testing, evaluation, and deployment. By ensuring the optimal development and performance of this system, women can effectively manage their menstrual blood, a vital aspect of their overall health. Moreover, this system holds particular significance for every women, as it facilitates tracking and identification of regular and irregular cycles, enabling them to uphold the legitimacy of their worship.

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