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-Neural network-based optimization of hydrogen fuel production energy system with proton exchange electrolyzer supported nanomaterial

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Fuel 2023年第Part1期332卷

作者： Hai, Tao Hikmat Hama Aziz, Kosar Zhou, Jincheng Dhahad, Hayder A. Sharma, Kamal Fahad Almojil, Sattam Ibrahim Almohana, Abdulaziz Fahmi Alali, Abdulrhman Ismail Kh, Teeba Mehrez, Sadok Abdelrahman, Anas School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun 558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Guizhou Duyun 558000 China nstitute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Selangor Shah Alam 40450 Malaysia Department of Chemistry College of Science University of Sulaimani Qlyasan Street Kurdistan Region 46001 Iraq Department of Medical Laboratory of Science College of Health Sciences University of Human Development Sulaimaniyah Iraq Mechanical Engineering Department University of Technology Baghdad Iraq Institute of Engineering and Technology GLA University Uttar Pradesh Mathura 281406 India Department of Civil Engineering College of Engineering King Saud University P.O. Box 800 Riyadh 11421 Saudi Arabia Department of Computer Engineering College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq Department of Mechanical Engineering College of Engineering at Al Kharj Prince Sattam bin Abdulaziz University 16273 Saudi Arabia Department of Mechanical Engineering University of Tunis El Manar ENIT BP 37 Le Belvédère Tunis 1002 Tunisia Department of Chemical Engineering Faculty of Engineering & Technology Future University in Egypt New Cairo 11500 Tunisia Key Laboratory of Complex Systems and Intelligent Optimization of Qiannan Duyun 558000 China

In this research, we try to investigate a solar-geothermal energy system. This system includes three turbines for power production, a PEM electrolyzer for hydrogen production, and a thermoelectric for generating electricity from excess heat. In addition, the seawater will be passed through the osmotic cycle to gain fresh water. The required power for this osmotic cycle will be obtained through the energy produced by the main turbines. The generated load, hydrogen production flow rate, purified water flow rate, and heating consumption are assessed in this study. The results showed that this system can produce 3.8 megawatts of electricity as well as 8 g per second of hydrogen fuel at the operating point. Also, the energy efficiency of this system is estimated to be 19%. Afterward, machine learning methods are used to optimize designing parameters, and the optimum operating point in terms of useful power and stored fuel flow rate is obtained by a genetic algorithm. The optimum operating point of this energy system has a useful power output of 4.099 megawatts and a hydrogen flow rate of 29 g per second. In the end, the distribution of the design parameters is displayed for points of the beam curve. © 2022

关键词： Biofuel Electrolyzer Fuel production Hydrogen fuel Optimization Solar energy

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Weakly supervised learning for textbook question answering

TechRxiv

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TechRxiv 2021年

作者： Ma, Jie Chai, Qi Huang, Jingyue Liu, Jun You, Yang Zheng, Qinghua National Engineering Lab for Big Data Analytics School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&D School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China The High Performance Computing for Artificial intelligence Lab Department of Computer Science National University of Singapore 117417 Singapore

Textbook Question Answering (TQA) is the task of answering diagram and non-diagram questions given large multimodal contexts consisting of abundant text and diagrams. Deep text understandings and effective learning of diagram semantics are important for this task due to its specificity. In this paper, we propose a Weakly Supervised learning method for TQA (WSTQ), which regards the incompletely accurate results of essential intermediate procedures for this task as supervision to develop Text Matching (TM) and Relation Detection (RD) tasks and then employs the tasks to motivate itself to learn strong text comprehension and excellent diagram semantics respectively. Specifically, we apply the result of text retrieval to build positive as well as negative text pairs. In order to learn deep text understandings, we first pre-train the text understanding module of WSTQ on TM and then fine-tune it on TQA. We build positive as well as negative relation pairs by checking whether there is any overlap between the items/regions detected from diagrams using object detection. The RD task forces our method to learn the relationships between regions, which are crucial to express the diagram semantics. We train WSTQ on RD and TQA simultaneously, i.e., multitask learning, to obtain effective diagram semantics and then improve the TQA performance. Extensive experiments are carried out on CK12-QA and AI2D to verify the effectiveness of WSTQ. Experimental results show that our method achieves significant accuracy improvements of 5.02% and 4.12% on test splits of the above datasets respectively than the current state-of-the-art baseline. We have released our code on https://***/dr-majie/WSTQ. © 2021, CC BY.

关键词： Semantics

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C-Net: A reliable convolutional neural network for biomedical image classification

arXiv

引用

arXiv 2020年

作者： Barzekar, Hosein Yu, Zeyun Big Data Analytics and Visualization Laboratory Department of Computer Science University of Wisconsin-Milwaukee MilwaukeeWI53211 United States Department of Biomedical Engineering University of Wisconsin-Milwaukee MilwaukeeWI53211 United States

Cancers are the leading cause of death in many countries. Early diagnosis plays a crucial role in having proper treatment for this debilitating disease. The automated classification of the type of cancer is a challenging task since pathologists must examine a huge number of histopathological images to detect infinitesimal abnormalities. In this study, we propose a novel convolutional neural network (CNN) architecture composed of a Concatenation of multiple Networks, called C-Net, to classify biomedical images. The model incorporates multiple CNNs including Outer, Middle, and Inner. The first two parts of the architecture contain six networks that serve as feature extractors to feed into the Inner network to classify the images in terms of malignancy and benignancy. The C-Net is applied for histopathological image classification on two public datasets, including BreakHis and Osteosarcoma. To evaluate the performance, the model is tested using several evaluation metrics for its reliability. The C-Net model outperforms all other models on the individual metrics for both datasets and achieves zero misclassification. This approach has the potential to be extended to additional classification tasks, as experimental results demonstrate utilizing extensive evaluation metrics. Copyright © 2020, The Authors. All rights reserved.

关键词： computer aided diagnosis

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Nuclei grading of clear cell renal cell carcinoma in histopathological image by composite high-resolution network

arXiv

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arXiv 2021年

作者： Gao, Zeyu Shi, Jiangbo Zhang, Xianli Li, Yang Zhang, Haichuan Wu, Jialun Wang, Chunbao Meng, Deyu Li, Chen School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Xi'an710049 China National Engineering Lab for Big Data Analytics Xi'an Jiaotong University Shaanxi Xi'an710049 China Department of Pathology The First Affiliated Hospital of Xi'an Jiaotong University Xi'an710061 China School of Mathematics and Statistics Xi'an Jiaotong University Shaanxi Xi'an710049 China

The grade of clear cell renal cell carcinoma (ccRCC) is a critical prognostic factor, making ccRCC nuclei grading a crucial task in RCC pathology analysis. computer-aided nuclei grading aims to improve pathologists' work efficiency while reducing their misdiagnosis rate by automatically identifying the grades of tumor nuclei within histopathological images. Such a task requires precisely segment and accurately classify the nuclei. However, most of the existing nuclei segmentation and classification methods can not handle the inter-class similarity property of nuclei grading, thus can not be directly applied to the ccRCC grading task. In this paper, we propose a Composite High-Resolution Network for ccRCC nuclei grading. Specifically, we propose a segmentation network called W-Net that can separate the clustered nuclei. Then, we recast the fine-grained classification of nuclei to two cross-category classification tasks, based on two high-resolution feature extractors (HRFEs) which are proposed for learning these two tasks. The two HRFEs share the same backbone encoder with W-Net by a composite connection so that meaningful features for the segmentation task can be inherited for the classification task. Last, a head-fusion block is applied to generate the predicted label of each nucleus. Furthermore, we introduce a dataset for ccRCC nuclei grading, containing 1000 image patches with 70945 annotated nuclei. We demonstrate that our proposed method achieves state-of-the-art performance compared to existing methods on this large ccRCC grading dataset. © 2021, CC BY.

关键词： Grading

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Instance-based vision transformer for subtyping of papillary renal cell carcinoma in histopathological image

arXiv

引用

arXiv 2021年

作者： Gao, Zeyu Hong, Bangyang Zhang, Xianli Li, Yang Jia, Chang Wu, Jialun Wang, Chunbao Meng, Deyu Li, Chen School of Computer Science and Technology Xi'an Jiaotong University Xi'an Shaanxi710049 China National Engineering Lab for Big Data Analytics Xi'an Jiaotong University Xi'an Shaanxi710049 China Department of Pathology The First Affiliated Hospital of Xi'an Jiaotong University Xi'an710061 China School of Mathematics and Statistics Xi'an Jiaotong University Xi'an Shaanxi710049 China

Histological subtype of papillary (p) renal cell carcinoma (RCC), type 1 vs. type 2, is an essential prognostic factor. The two subtypes of pRCC have a similar pattern, i.e., the papillary architecture, yet some subtle differences, including cellular and cell-layer level patterns. However, the cellular and cell-layer level patterns almost cannot be captured by existing CNN-based models in large-size histopathological images, which brings obstacles to directly applying these models to such a fine-grained classification task. This paper proposes a novel instance-based Vision Transformer (i-ViT) to learn robust representations of histopathological images for the pRCC subtyping task by extracting finer features from instance patches (by cropping around segmented nuclei and assigning predicted grades). The proposed i-ViT takes top-K instances as input and aggregates them for capturing both the cellular and cell-layer level patterns by a position-embedding layer, a gradeembedding layer, and a multi-head multi-layer self-attention module. To evaluate the performance of the proposed framework, experienced pathologists are invited to selected 1162 regions of interest from 171 whole slide images of type 1 and type 2 pRCC. Experimental results show that the proposed method achieves better performance than existing CNN-based models with a significant margin. © 2021, CC BY.

关键词： Formal languages

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A ROBUST REPUTATION-BASED GROUP RANKING SYSTEM AND ITS RESISTANCE TO BRIBERY

arXiv

引用

arXiv 2020年

作者： Saúde, João Ramos, Guilherme Boratto, Ludovico Caleiro, Carlos Department of Electrical and Computer Engineering Carnegie-Melon University Pittsburgh United States Department of Electrical and Computer Engineering Faculty of Engineering University of Porto Porto Portugal Data Science and Big Data Analytics EURECAT Barcelona Spain SQIG - Instituto de Telecomunicações Dept. of Mathematics Instituto Superior Técnico University of Lisbon Lisbon Portugal

The spread of online reviews and opinions and its growing influence on people’s behavior and decisions, boosted the interest to extract meaningful information from this data deluge. Hence, crowdsourced ratings of products and services gained a critical role in business and governments. Current state-of-the-art solutions rank the items with an average of the ratings expressed for an item, with a consequent lack of personalization for the users, and the exposure to attacks and spamming/spurious users. Using these ratings to group users with similar preferences might be useful to present users with items that reflect their preferences and overcome those vulnerabilities. In this paper, we propose a new reputation-based ranking system, utilizing multipartite rating subnetworks, which clusters users by their similarities using three measures, two of them based on Kolmogorov complexity. We also study its resistance to bribery and how to design optimal bribing strategies. Our system is novel in that it reflects the diversity of preferences by (possibly) assigning distinct rankings to the same item, for different groups of users. We prove the convergence and efficiency of the system. By testing it on synthetic and real data, we see that it copes better with spamming/spurious users, being more robust to attacks than state-of-the-art approaches. Also, by clustering users, the effect of bribery in the proposed multipartite ranking system is dimmed, comparing to the bipartite case. Copyright © 2020, The Authors. All rights reserved.

关键词： data mining

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The role of usability on e-learning user interactions and satisfaction: a literature review

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Journal of Systems and Information Technology 2019年第3期21卷 368-394页

作者： Gunesekera, Asela Indunil Bao, Yukun Kibelloh, Mboni Center for Modern Information Managment Huazhong University of Science and Technology Wuhan China Center for Big Data Analytics Jiangxi University of Engineering Xinyu China Faculty of Arts and Social Science Tumaini University Makumira – Dar es Salaam College Dar es Salaam Tanzania United Republic of Department of Computer Science General Sir John Kotelawala Defence University Ratmalana Sri Lanka

Purpose: The purpose of this study is to review the effect of usability factors on e-learning user relationships, namely, student–student interaction (SSI), student–instructor interaction (SII) and student–content interaction (SCI), in the existing e-learning literature. Further, this study intended to identify whether usability contributes to the satisfaction of e-learners. Design/methodology/approach: This study has undertaken a systematic review using the PRISMA methodology to filter the literature in the domain of e-learning with respect to usability concerns using six databases. An analytical framework has been formulated to evaluate the literature against different dimensions of interactions and usability. Findings: Results reveal that while SSI has grabbed 71.4 per cent research attention with respect to usability factors of e-learning systems, SCI has been given the least focus, i.e. 26.6 per cent. According to the results, e-learning systems’ usability issues influence the user relationships and affect the user satisfaction, which will lead to lack of user continuity. Practical implications: The findings of this review will provide insights to instructional designers to construct more satisfied learning content for the users. The analysis framework of this study will encourage researchers to drive future research in e-learning along with the concern of usability. Originality/value: This research emphasizes on the importance of SCI to focus future e-learning research on a different angle, in addition to SSI and SII. The analysis framework of this study will provide different dimensions, specifically for the empirical research in the domain of e-learning. © 2019, Emerald Publishing Limited.

关键词： Students

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Correction to: Framework for automatically suggesting remedial actions to help students at risk based on explainable ML and rulebased models

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International Journal of Educational Technology in Higher Education 2022年第1期19卷 1-1页

作者： Albreiki, Balqis Habuza, Tetiana Zaki, Nazar Department of Computer Science and Software Engineering College of Information Technology UAE University Al Ain UAE Office of Institutional Effectiveness UAE University Al Ain UAE Big Data Analytics Center (BIDAC) UAE University Al Ain UAE

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Identification of new disease genes from protein–protein interaction network

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Journal of Ambient Intelligence and Humanized Computing 2018年 1-9页

作者： Masood, M. Mohamed Divan Manjula, D. Sugumaran, Vijayan Department of Computer Science and Engineering Anna University Chennai India Center for Data Science and Big Data Analytics Department of Decision and Information Sciences School of Business Administration Oakland University RochesterMI48309 United States

Human beings are susceptible to diseases caused by a variety of reasons. The causal factors for diseases include microorganisms (viruses and bacteria), environmental factors and largely, genetics. As most diseases are caused by the genes of a particular living organism, it is necessary to analyze the genetic characteristics to treat the disease effectively. The genetics analysis is done in this research using protein–protein interaction network (PPI), which contains information on a gene’s interaction with other genes of a particular living organism. Using the PPI network, new genes for a particular disease can be discovered by applying the shortest path algorithm on the PPI network, given that genes reside next to one another in the network. Consequently, the shortest path algorithm that is executed on the PPI network will retrieve the most similar genes that are related to a particular disease. From the retrieved candidate genes, irrelevant genes are filtered out and the remaining are taken as novel genes. © 2018 Springer-Verlag GmbH Germany, part of Springer Nature

关键词： Genes

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Meta mask correction for nuclei segmentation in histopathological image

arXiv

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arXiv 2021年

作者： Shi, Jiangbo Jia, Chang Gao, Zeyu Gong, Tieliang Wang, Chunbao Li, Chen National Engineering Lab for Big Data Analytics Xi'an Jiao Tong University Shaanxi Xi'an710049 China School of Computer Science and Technology Xi'an Jiao tong University Shaanxi Xi'an710049 China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech.R&D Xi'an Jiao tong University Shaanxi Xi'an710049 China Department of Pathology The First Affiliated Hospital of Xi'an Jiaotong University Shaanxi Xi'an710061 China

Nuclei segmentation is a fundamental task in digital pathology analysis and can be automated by deep learning-based methods. However, the development of such an automated method requires a large amount of data with precisely annotated masks which is hard to obtain. Training with weakly labeled data is a popular solution for reducing the workload of annotation. In this paper, we propose a novel meta-learning-based nuclei segmentation method which follows the label correction paradigm to leverage data with noisy masks. Specifically, we design a fully conventional meta-model that can correct noisy masks using a small amount of clean meta-data. Then the corrected masks can be used to supervise the training of the segmentation model. Meanwhile, a bi-level optimization method is adopted to alternately update the parameters of the main segmentation model and the meta-model in an end-to-end way. Extensive experimental results on two nuclear segmentation datasets show that our method achieves the state-of-the-art result. It even achieves comparable performance with the model training on supervised data in some noisy settings. © 2021, CC BY.

关键词： Deep learning

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