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Predicting metal-organic frameworks as catalysts to fix carbon dioxide to cyclic carbonate by machine learning

Journal of Materiomics 2021年第5期7卷 1029-1038页

作者： Shuyuan Li Yunjiang Zhang Yuxuan Hu Bijin Wang Shaorui Sun Xinwu Yang Hong He Beijing Key Laboratory for Green Catalysis and Separation The Faculty of Environment and LifeBeijing University of TechnologyBeijing100124China School of Software Engineering Beijing University of TechnologyBeijing100124China Faculty of Information Beijing University of TechnologyBeijing100124China

The process of discovering and developing new materials currently requires considerable effort,time,and *** learning(ML)algorithms can potentially provide quick and accurate methods for screening new *** the present work,the features of the metal organic frameworks(MOFs)as a catalyst for fixing carbon dioxide into cyclic carbonate were extracted to build a data set,which were collected from the experimental results of approximately 100 published *** were trained with the data set with various ML algorithms,including support vector machine(SVM),K-nearest neighbor classification(KNN),decision trees(DT),stochastic gradient descent(SGD),and neural networks(NN),to predict the catalytic *** ML models were trained on 80% of the data set and then tested on the remaining 20%to predict the carbon dioxide fixation *** trained ML model was extended to explore 1311 hypothetical MOFs,and some structures displayed a strong catalytic ***,the six best metal ions(Mn,V,Cu,Ni,Zr and Y)and four best ligands(tactmb,tdcbpp,TCPP,H_(3)L)were *** six metals and four ligands could be combined into 24 MOFs,which are strongly potential catalysts for carbon dioxide *** machine learning methods can speed up the screening of materials,and this methodology is promising for application not only to MOFs as catalysts but also in many other materials science projects.

关键词： Machine learning Metal-organic frameworks Catalysts CO_(2)fixation Cyclic carbonate

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Optimizing Gammatone Cepstral Coefficients for Gear Fault Detection

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IEEE Access 2025年 13卷 101711-101720页

作者： Zrar Kh Abdul Abdulbasit K. Al-Talabani Wisam Hazim Gwad Entisar Alkayal Halgurd S. Maghdid Safar Maghdid Asaad Computer Department College of Science Charmo University Sulaymaniyah Iraq Department of Software Engineering Faculty of Engineering Koya University Koya Iraq Department of Artificial Intelligence Engineering College of Engineering Alnoor University Ninavah Iraq Information Technology Department Faculty of Computing and Information Technology-Rabigh King Abdulaziz University Jeddah Saudi Arabia Department of Engineering Research Center Deanship of Research and Development Centers Koya University Erbil Governorate Iraq Department of Computer Engineering College of Engineering Knowledge University Erbil Iraq

Cepstral features, such as Gammatone Cepstral Coefficients (GTCC), have recently been applied in fault detection and diagnosis. However, GTCC was originally designed for speech feature extraction rather than fault detection, which limits its ability to capture relevant features for fault identification effectively. In this research, three key parameters of GTCC, namely, the number of coefficients, maximum frequency, and minimum frequency, are optimized using two metaheuristic algorithms: Fitness Dependent Optimizer (FDO) and Grey Wolf Optimization (GWO). These parameters are vital for enhancing the effectiveness of GTCC in extracting relevant features from the vibration signal for gear defect identification. Specifically, the maximum and minimum frequency values are critical for capturing the Gear Mesh Frequency (GMF), a common indicator of gear faults. Furthermore, optimizing the number of GTCC coefficients helps reduce model complexity. Experimental results demonstrate that optimizing GTCC parameters with GWO improves fault detection performance using an SVM classifier, achieving over 1% and 3% accuracy improvements on the PHM09 and DDS datasets, respectively.

关键词： Feature extraction Fault detection Optimization Gears Metaheuristics Mel frequency cepstral coefficient Mathematical models Filter banks Support vector machines Cepstrum

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Eatsa-Gnn: Edge-Aware and Two-Stage Attention for Enhancing Graph Neural Networks Based on Teacher-Student Mechanisms for Graph Node Classification

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SSRN 2024年

作者： Fofanah, Abdul Joseph Leigh, Alpha Omar School of Information and Communication Technology Griffith University 170 Kessels Road BrisbaneQLD4111 Australia Department of Software Engineering with Multimedia Faculty of Information Communication and Technology Limkokwing University Freetown Sierra Leone

Graph Neural Networks (GNNs) have fundamentally transformed the way in which we handle and examine data originating from non-Euclidean domains. Traditional approaches to imbalanced node classification problems, such as resampling, are ineffective because they do not take into account the underlying network structure of the edges. The limited methods available to capture the intricate connections encoded in the edges of a graph pose a significant challenge for GNNs in accurately classifying nodes. We propose EATSA model to enhance GNN node classification using Edge-Aware and Two-Stage Attention Mechanisms (EATSA). EATSA focuses its initial attention on edge traits, enabling the model to differentiate the variable significance of different connections between nodes, referred to as Teacher-Attention (TA). In the second step, attention is directed towards the nodes, incorporating the knowledge obtained from the edge-level analysis referred to as Student-Attention (SA). Employing this dual strategy ensures a more sophisticated comprehension of the graph's framework, resulting in improved classification precision. The EATSA model's contribution to the field of GNNs lies in its ability to utilise both node and edge information in a cohesive manner, resulting in more accurate node classifications. This highlights the essence of the model and its potential. Comparing the EATSA model to state-of-the-arts methods with two different variants shows how strong it is and how well it can handle complex problems for node classification. This solidifies its position as one of leading solution in the field of GNN architectures and their use in complex networked systems. The exceptional performance of EATSA not only showcases its effectiveness but also underscores its potential to greatly influence the future advancement of the GNN framework. © 2024, The Authors. All rights reserved.

关键词： Classification (of information)

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Sports Visual Data Analysis with Deep Vision 22

Sports Visual Data Analysis with Deep Vision

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11th International Symposium on information and Communication technology, SoICT 2022

作者： Huynh, Minh-Hieu Bui-Xuan, Bao Tran, Minh-Triet Faculty of Information Technology University of Science VNU-HCM Viet Nam National University Ho Chi Minh City Viet Nam Faculty of Information Technology Software Engineering Lab University of Science VNU-HCM Viet Nam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9781450397254

The sports industry is surrounded by opportunities in all facets, including sports marketing, sports media, education institutions, sponsorship, and so on. They are adopting the newest technology such as computer vision to improve coaching task as well as benefit the sport broadcasting industry. In this paper, we examine action spotting in sports, particularly soccer. We found the SoccerNet challenge, which is a soccer video understanding challenge. SoccerNet provides a dataset for action spotting in soccer of 550 videos of famous leagues in recent years along with the event annotations in 17 classes. Action spotting is a concept that SoccerNet defined as finding the anchor time (or spot) that identifies an event. Inspired by previous successful methods under the action spotting task NetVLAD++ and AudioVid, we propose a multimodal architecture that learns temporal features of both visual and audio representation of video data. Our method achieves an mAP of 56.58%, which rises 3.18% higher than NetVLAD++ and 16.68% higher than AudioVid. Furthermore, we integrate that research into a web-based platform in order to contribute to the annotation industry. This platform consists of a client application written in ReactJS and a backend server. Both of them are deployed on the Google Cloud Platform so that users can experience our platform on any device without complex installation. © 2022 ACM.

关键词： Sports

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Industrial Automation information Analogy for Smart Grid Security

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Computers, Materials & Continua 2022年第5期71卷 3985-3999页

作者： Muhammad Asif Ishfaq Ali Shahbaz Ahmad Azeem Irshad Akber Abid Gardezi Fawaz Alassery Habib Hamam Muhammad Shafiq Department of Computer Science National Textile UniversityFaisalabad37610PunjabPakistan Department of Computer Science and Software Engineering International Islamic UniversityIslamabadPakistan Department of Computer Science COMSATS University IslamabadIslamabadPakistan Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia Faculty of Engineering Moncton UniversityNBE1A3E9Canada Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Industrial automation or assembly automation is a strictly monitored environment,in which changes occur at a good *** are many types of entities in the focusing environment,and the data generated by these devices is *** addition,because the robustness is achieved by sensing redundant data,the data becomes *** data generating device,whether it is a sensing device or a physical device,streams the data to a higher-level deception device for calculation,so that it can be driven and configured according to the updated *** the emergence of the Industry 4.0 concept that includes a variety of automation technologies,various data is generated through numerous ***,the data generated for industrial automation requires unique information Architecture(IA).IA should be able to satisfy hard real-time constraints to spontaneously change the environment and the instantaneous configuration of all *** understand its applicability,we used an example smart grid *** smart grid system needs an IA to fulfill the communication requirements to report the hard real-time changes in the power immediately following the *** addition,in a smart grid system,it needs to report changes on either side of the system,i.e.,consumers and suppliers configure and reconfigure the system according to the *** this article,we propose an analogy of a physical phenomenon.A point charge is used as a data generating device,the streamline of electric flux is used as a data flow,and the charge distribution on a closed surface is used as a ***,the intensity changes are used in the physical process,e.g.,the smart *** analogy is explained by metaphors,and the structural mapping framework is used for its theoretical *** proposed analogy provides a theoretical basis for the development of such information architectures that can represent data flows,definition changes(deterministic and non-deterministic),events,and i

关键词： Industry 4.0 information architecture industrial automation

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Analysis of the Gap between software Testing Courses at Universities and the Needed Skills by Industry

SSRN

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SSRN 2022年

作者： Hanna, Samer Odeh Software Engineering Department Faculty of Information Technology Philadelphia University Jordan

software testing courses at universities are supposed to provide students with the required skills to join software testing jobs in industry. However, there is a discrepancy between the topics of these courses and the skills needed by companies in the field of software testing. Many of the software testing skills that are highly required by employers are not covered by many testing courses. This problem will lead to university graduates that are not qualified enough to join testing jobs in industry. To solve this problem, this research provides comparison an empirical analysis of the topics provided by 80 software testing courses in 9 countries around the world and the skills that are required by 400 software testing related job advertisements in the same investigated countries. Based on this analysis, the research provides a taxonomy of the testing topics by universities and the needed skills by industry. The main knowledge gaps between testing courses and job ads are explained in details. Providing data analysis of the required testing skills by industry and comparing these skills with the topics of testing courses will help universities to modify these courses to make them closer to the industrial needs. © 2022, The Authors. All rights reserved.

关键词： software testing

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On Students’ Sentiment Prediction Based on Deep Learning: Applied information Literacy

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SN Computer Science 2024年第7期5卷 928页

作者： Tuan, Nguyen Minh Meesad, Phayung Hieu, Duong Van Cuong, Nguyen Ha Huy Maliyaem, Maleerat Department of Mathematics and Computer Science FernUniversität in Hagen Universitätsstraße 47 Hagen Hagen 58097 Germany Department of Information Technology and Management King Mongkut’s University of Technology North Bangkok Pracharat Bangkok Bangsue 10800 Thailand Faculty of Engineering Technology Tien Giang University Ap Bac Tien Giang My Tho 860000 Viet Nam Software Development Centre University of Danang Le Duan Danang Hai Chau 50000 Viet Nam Department of Information Technology King Mongkut’s University of Technology North Bangkok Pracharat Bangkok Bangsue 10800 Thailand

The main purpose of this study is to understand the assessment of students’ sentiments on improving the role of library service quality after the pandemic and deduce the trends consistent with the new phase of technology. In this study, convolution bidirectional learners (CBLs) are employed for evaluating such as Convolution Bidirectional Long Short-Term Memory (CBLSTM), Gated Recurrent Unit (CBGRU), Simple Recurrent Neural Network (CBSRNN), Combination of Convolutional Bidirectional LSTM+GRU (CBLSTMGRU), SimpleRNN+LSTM (CBSLSTM), SimpleRNN+GRU (CBSGRU), Attention, and Transformer TFBERT model. The results show that CBSLSTM (94.82% accuracy) and CBLSTMGRU (94.78% accuracy) surpassed the other models in predicting Vietnamese students’ sentiments. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Attention Convolution model GRU Library service quality LSTM Transformer TfBERT

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Healthcare Professionals’ Perceptions Towards Ai Integration in Jordan's Health Sector

SSRN

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SSRN 2024年

作者： Klaib, Ahmad F. Alhosanie, Tasneem N. Mahdin, Ladees W. Altatar, Enas J. Software Engineering Department King Hussein School of Computing Sciences Princess Sumaya University for Technology Amman Jordan Information Systems Department Faculty of Information Technology and Computer Sciences Yarmouk University Irbid Jordan

PurposeThe impact of AI on healthcare is widely recognized there remains a scarcity of studies examining how doctors perceive and approach its use in medicine. This study aims to gather insights from healthcare providers in Jordan concerning the advantages of integrating AI into practices, their perspectives on AI applications in healthcare, and their views on the future role of AI in replacing key tasks within health *** survey was conducted among healthcare professionals working at facilities in Jordan. An online questionnaire was used to collect data on demographics, attitudes toward using AI for tasks, and opinions on the benefits of AI adoption. Categorical variables were presented as counts and percentages, while the continuous variables were interpreted as mean and standard deviation. The associations between the determinants and the outcomes were done using one-way ANOVA. Any test with a P-value 0.05 was considered *** total of 612 healthcare professionals participated in the survey with females comprising a majority of respondents (52.8%). The majority of respondents showed optimism about AI’s potential to improve and revolutionize the field, although there were concerns about AI replacing human roles. Generally, physical therapists, medical researchers, and pharmacists displayed openness to incorporating AI into their work routines. Younger individuals aged between 18 and 40 seemed accepting of AI in the domain. A significant portion of participants believed that AI could negatively impact job opportunities and reduce the time needed for diagnosing conditions, but did not find any correlation, between responses and *** conclude, the results of this study suggest that healthcare professionals, in Jordan, hold receptive views on incorporating artificial intelligence in the medical field similar to their counterparts in developed nations. However, there is a concern about the implications of AI, on job stability a

关键词： Artificial intelligence

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Cloud Sentiment Accuracy Comparison using RNN, LSTM and GRU

Cloud Sentiment Accuracy Comparison using RNN, LSTM and GRU

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2021 Innovations in Intelligent Systems and Applications Conference, ASYU 2021

作者： Raza, Muhammad Raheel Hussain, Walayat Merigo, Jose Maria Firat University Department of Software Engineering Elazig Turkey University of Technology Sydney Faculty of Engineering Information Technology SydneyNSW2007 Australia

ISBN: (纸本)9781665434058

Cloud computing has become a de facto choice of many individuals and enterprises for computing solutions. In the last few years, many cloud providers appear in the market that offers the same services. It is a trivial job to choose an optimal service best suited for organisations in such a massive arms race of service providers. Existing consumer experience could help significantly build a holistic perception of their experiences that ultimately influence service adoption decisions. Sentiment analysis is an effective tool to understand consumer experience about the product or service. The sophisticated sentiment analysis could help businesses to gain a better insight and respond proactively to consumer issues. There are various methods for sentiment analysis that produces ideal results under different conditions. Therefore, it is very important to choose the right method to predict consumer's sentiment for a greatest result. In this paper we analyse the sentiment prediction accuracy of widely used neural network methods - recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent network (GRU). We use software as a service (SaaS) dataset having 6258 reviews. From analysis results we find that GRU outperforms the LSTM and RNN methods. © 2021 IEEE.

关键词： Long short-term memory

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Long Short-Term Memory-based Sentiment Classification of Cloud Dataset

Long Short-Term Memory-based Sentiment Classification of Clo...

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2021 Innovations in Intelligent Systems and Applications Conference, ASYU 2021

ISBN: (纸本)9781665434058

Text Sentiment Classification is a crucial task for various decision-making processes in many organizations. It identifies the polarity of texts positively and negatively and highlights the opinions and views hidden within the comments or reviews of a product or service. Performing it on big data from social media and related sources is quite tricky and time-consuming. Nowadays, Deep Learning (DL) is widely used for sentiment analysis due to its high performance. In this paper, Recurrent Neural Network (RNN) based Long Short-Term Memory (LSTM) approach is applied to perform sentiment analysis of a cloud review dataset. The cloud review dataset contains cloud consumer reviews regarding the services provided by different cloud service providers and the dataset is achieved as a result of the Harvesting-as-a-Service (HaaS) framework. The study focuses on observing the behaviour of the deep learning RNN-LSTM approach on a cloud dataset. Results of the experiment are evaluated using various evaluation and performance metrics. The approach tends to achieve 95 % accuracy. © 2021 IEEE.

关键词： Long short-term memory

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