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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Ray, Palash Bera, Asish Krejcar, Ondrej Bhattacharjee, Debotosh Nasipuri, Mita Department of Computer Science and Engineering Haldia Institute of Technology West Bengal Haldia India Department of Computer Science and Information Systems BITS Pilani Rajasthan Pilani India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India

ISBN: (纸本)9789819726134

Sport action recognition is an interesting area in computer vision. Categorization of sport actions, representing difficult and complex body postures, is regarded as a fine-grained visual classification problem. The Convolutional Neural Networks (CNNs) have attained enhanced performance over conventional feature descriptors in recognizing various sport activities. In general, though decent improvement has been gained using deep learning for sport action recognition, however, recognition of women’s sport activities is not widely explored. Even, no benchmark dataset depicting women’s sport action with sufficient variations is available yet for study. Hence, fine-grained image classification of diverse sport categories involving female/women athletics requires immediate research attention. To overcome this limitation, this paper proposes an image dataset comprising worldwide popular 50 sport categories of women players only. A simple deep learning model is proposed that extracts the high-level deep features using a backbone CNN. Then, these features are pooled from a collection of regular regions representing local discriminative information. The spatial pyramid pooling is applied for mining semantic information and enhancing feature aggregation for classification. The proposed method has achieved satisfactory performance on the Women Sports dataset using four standard backbone CNNs. Moreover, our method has achieved better accuracy on the Yoga-82 pose recognition dataset with a significant margin, e.g., 11.6% gain using ResNet-50 base CNN. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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First principle study of X2GaAgCl6 (X = Cs, Rb) double perovskites: structural, mechanical, vibrational, electronic, optical, SLME, thermoelectric, and thermodynamic properties for solar cell applications

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Environmental science and Pollution Research 2024年第24期31卷 34881-34895页

作者： Rehman, Zia Ur Rehman, Muhammad Awais Khan, Muhammad Rashid Rehman, Bushra Sikiru, Surajudeen Rizwan, Muhammad Chafi, Mohammed Usman, Muhammad Department of Mathematics Namal University 30Km Talagang Road Mianwali42250 Pakistan Institute of Physics University of Silesia Ul 75 Pulku Piechoty 1 Chorzów41-500 Poland Department of Environmental Science Quaid-I-Azam University Islamabad Islamabad45320 Pakistan School of Physics and Materials Studies Faculty of Applied Science Universiti Teknologi Mara Selangor Shah Alam40450 Malaysia School of Physical Sciences University of the Punjab Lahore Pakistan LIPE Higher School of Technology Hassan II University of Casablanca Oasis Morocco Department of Applied Mechanics School of Mathematics and Physics University of Science and Technology Beijing Beijing China

The structural, mechanical, vibrational, electronic, optical, SLME, thermoelectric, and thermodynamic properties of X2GaAgCl6 (X = Cs, Rb), a double perovskite material, were computed by employing Density Functional Theory (DFT). CASTEP and Quantum ESPRESSO were used to perform first-principles calculations. X2GaAgCl6 possesses a cubic structure with the space-group symmetry Fm-3 m. The lattice parameters of Cs2GaAgCl6 and Rb2GaAgCl6 were optimized using the energy-volume curves, resulting in values of 7.357 Å and 7.365 Å, respectively. The population analysis confirmed the charge transfer among transition metals and halogen atoms. The stability of crystal X2GaAgCl6 (X = Cs, Rb) is effectively demonstrated by analyzing phonon dispersion curves with no negative frequencies. The band structure calculations indicated the semiconducting nature of compounds with energy gaps of 0.96 eV and 0.88 eV for Cs2GaAgCl6 and Rb2GaAgCl6, respectively. The optical characteristics results confirm that the examined materials are suitable for devices working, primarily in the electromagnetic spectrum’s visible region. SLME results showed that Cs2GaAgCl6 has 30% and Rb2GaAgCl6 has 27% efficiency, respectively, suggesting their use in photovoltaics. The thermoelectric properties of X2GaAgCl6 (X = Cs, Rb) were calculated by using the BoltzTraP code in the temperature range of 300 to 800 K. The quasi-harmonic Debye model was applied to calculate the thermodynamic characteristics. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Density functional theory

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Quantum Network Coding and Distribution of Maximally Entangled States in Measurement-Based Quantum Computing

Quantum Network Coding and Distribution of Maximally Entangl...

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2023 National Conference on Communications, NCC 2023

作者： Pandey, Cheeranjiv Gupta, Sanidhya Das, Rimon Ranjit Raina, Ankur Indian Institute of Technology Department of Physics Ropar India Indian Institute of Science Education and Research Department of Electrical Engineering and Computer Science Bhopal India Amity University Amity Institute of Applied Sciences Department of Physics India

ISBN: (纸本)9781665456258

Classical network coding uses encoding of information over networks to achieve secure and high throughput communication with low latency. Its quantum analog, namely Quantum Network Coding (QNC) is a promising protocol to achieve quantum communication over quantum networks. In this paper, we propose a method for establishing QNC in Measurement-Based Quantum Computing (MBQC), which is a universal quantum computational model. We demonstrate that the problem of multiple unicast or multiple multicast of distinct computational basis states over a quantum network can be solved by simulating existing classical networks, with the CNOT implementation in MBQC as a basis for transmitting and encoding of states. We also show how it can be further extended to simultaneously distribute the Bell pairs and Greenberger-Horne-Zeilinger (GHZ) states required for quantum communication between distant transmitter and corresponding receiver nodes of a network. © 2023 IEEE.

关键词： Network coding

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Boosting the electrical output of cement-based triboelectric nanogenerator by surface treatment technique 18

Boosting the electrical output of cement-based triboelectric...

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18th Siam physics Congress, SPC 2023

作者： Kaewsrithong, Kanisorn Kaewpoonsuk, Anucha Torkittikul, Pincha Wongkeo, Watcharapong Chaipanich, Arnon Rianyoi, Rattiyakorn Nochaiya, Thanongsak Department of Physic Faculty of Science Naresuan University Phitsanulok65000 Thailand Research Center for Academic Excellence in Applied Physics Naresuan University Phitsanulok65000 Thailand Department of Civil Technology Faculty of Industrial Technology Lampang Rajabhat University Lampang52100 Thailand Physics and General Science Program Faculty of Science and Technology Nakhon Ratchasima Rajabhat University Nakhon Ratchasima30000 Thailand Department of Physics and Materials Science Faculty of Science Chiang Mai University Chiang Mai50200 Thailand Center of Excellence in Materials Science and Technology Chiang Mai University Chiang Mai50200 Thailand

Triboelectric nanogenerators have the potential to convert mechanical energy to electric energy through electrostatic induction thus they are promising alternative cleaning energy sources for human beings. The objective of this work is to increase the electrical outputs of cement-based triboelectric nanogenerators (C-TENGs) by using a surface polishing treatment technique. The hand-mixed cement pastes with water-to-cement ratios of 0.3, 0.4, and 0.5 by weight were cast in the molds and cured in saturated lime water. A study was conducted to investigate the electrical output, which included voltage and current, under a single vertical contract-separation mode with a constant force and a frequency of 3 Hz. To improve the electrical generating performance, all samples were subjected to a surface treatment by polishing with sandpaper. After electrical investigations, the electrical outputs were remeasured. The results showed that the modified surface samples have significantly higher electrical outputs than that of the pristine sample due to the increased contact surface area. Moreover, the cement paste with a water-to-cement ratio of 0.3 after a polished-surface treatment exhibits the highest power density for an external resistance of 10 MΩ, improving around 70% higher than the pristine sample. The study successfully presents a low-cost and simple technique for improving the performance of cement-based triboelectric nanogenerators. © 2023 Institute of physics Publishing. All rights reserved.

关键词： Cements

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Remarkable synergistic effect in cobalt-iron nitride/alloy nanosheets for robust electrochemical water splitting

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Journal of Energy Chemistry 2022年第2期31卷 405-414页

作者： Mingpeng Chen Di Liu Baoye Zi Yuyun Chen Dong Liu Xinyu Du Feifei Li Pengfei Zhou Ye Ke Jielei Li Kin Ho Lo Chi Tat Kwok Weng Fai Ip Shi Chen Shuangpeng Wang Qingju Liu Hui Pan Institute of Applied Physics and Materials Engineering University of MacaoMacaoChina Yunnan Key Laboratory for Micro/Nano Materials&Technology National Center for International Research on Photoelectric and Energy MaterialsSchool of Materials and EnergyYunnan UniversityKunming 650091YunnanChina Department of Electromechanical Engineering Faculty of Science and TechnologyUniversity of MacaoMacaoChina Department of Physics and Chemistry Faculty of Science and TechnologyUniversity of MacaoMacaoChina

Design and synthesis of noble-metal-free bifunctional catalysts for efficient and robust electrochemical water splitting are of significant importance in developing clean and renewable energy sources for sustainable energy ***,a simple three-step strategy is reported to construct cobalt-iron nitride/alloy nanosheets on nickel foam(CoFe-NA/NF)as a bifunctional catalyst for both hydrogen evolution reaction(HER)and oxygen evolution reaction(OER).The electrocatalyst with optimized composition(CoFe-NA2/NF)can achieve ultralow overpotentials of 73 mV and 250 mV for HER and OER,respectively,at a current density of 10 mA cm^(-2) in 1 M ***,the electrolyzer based on this electrocatalyst is able to boost the overall water splitting with a cell voltage of 1.564 V to deliver 10 mA cm^(-2) for at least 50 h without obvious performance ***,our experiment and theoretical calculation demonstrate that the combination of cobalt-iron nitride and alloy can have low hydrogen adsorption energy and facilitate water dissociation during *** addition,the surface reconstruction introduces metal oxyhydroxides to optimize the OER *** work may pave a new pathway to design bifunctional catalysts for overall water splitting.

关键词： Bifunctional electrocatalyst Metal nitride/alloy nanocomposite Overall water splitting High performance

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Comparative Analysis of Machine Learning Algorithms for Email Phishing Detection Using TF-IDF, Word2Vec, and BERT

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computers, Materials & Continua 2024年第11期81卷 3395-3412页

作者： Arar Al Tawil Laiali Almazaydeh Doaa Qawasmeh Baraah Qawasmeh Mohammad Alshinwan Khaled Elleithy Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan College of Engineering Abu Dhabi UniversityAbu DhabiAl AinP.O.Box 1790United Arab Emirates Faculty of Artificial Intelligence Al-Balqa Applied UniversitySalt19117Jordan Department of Civil and Construction Engineering Western Michigan UniversityKalamazooMI 49008USA MEU Research Unit Middle East UniversityAmman11831Jordan Department of Computer Science and Engineering University of BridgeportBridgeportCT 06604USA

Cybercriminals often use fraudulent emails and fictitious email accounts to deceive individuals into disclosing confidential information,a practice known as *** study utilizes three distinct methodologies,Term Frequency-Inverse Document Frequency,Word2Vec,and Bidirectional Encoder Representations from Transform-ers,to evaluate the effectiveness of various machine learning algorithms in detecting phishing *** study uses feature extraction methods to assess the performance of Logistic Regression,Decision Tree,Random Forest,and Multilayer Perceptron *** best results for each classifier using Term Frequency-Inverse Document Frequency were Multilayer Perceptron(Precision:0.98,Recall:0.98,F1-score:0.98,Accuracy:0.98).Word2Vec’s best results were Multilayer Perceptron(Precision:0.98,Recall:0.98,F1-score:0.98,Accuracy:0.98).The highest performance was achieved using the Bidirectional Encoder Representations from the Transformers model,with Precision,Recall,F1-score,and Accuracy all reaching *** study highlights how advanced pre-trained models,such as Bidirectional Encoder Representations from Transformers,can significantly enhance the accuracy and reliability of fraud detection systems.

关键词： Attacks email phishing machine learning security representations from transformers(BERT) text classifeir natural language processing(NLP)

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Demand Forecasting of a Microgrid-Powered Electric Vehicle Charging Station Enabled by Emerging Technologies and Deep Recurrent Neural Networks

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computer Modeling in Engineering & sciences 2025年第5期143卷 2237-2259页

作者： Sahbi Boubaker Adel Mellit Nejib Ghazouani Walid Meskine Mohamed Benghanem Habib Kraiem Department of Computer and Network Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia The International Centre for Theoretical Physics Trieste UniversityTrieste34151Italy Faculty of Science and Technology University of JjelJijel18000Algeria Department of Civil Engineering College of EngineeringNorthern Border University Arar1321Saudi Arabia SAMATWAIQ for Drones Company Prince Sultan Roadeddah23621Saudi Arabia Physics Department Faculty of ScienceIslamic University of MadinahMadinah4235Saudi Arabia Department of Electrical Engineering College of EngineeringNorthern Border UniversityArar91431Saudi Arabia Center for Scientifc Research and Entrepreneurship Northern Border UniversityArar73213Saudi Arabia

Electric vehicles(EVs)are gradually being deployed in the transportation *** they have a high impact on reducing greenhouse gas emissions,their penetration is challenged by their random energy demand and difficult scheduling of their optimal *** cope with these problems,this paper presents a novel approach for photovoltaic grid-connected microgrid EV charging station energy demand *** present study is part of a comprehensive framework involving emerging technologies such as drones and artificial intelligence designed to support the EVs’charging scheduling *** using predictive algorithms for solar generation and load demand estimation,this approach aimed at ensuring dynamic and efficient energy flow between the solar energy source,the grid and the electric *** main contribution of this paper lies in developing an intelligent approach based on deep recurrent neural networks to forecast the energy demand using only its previous ***,various forecasters based on Long Short-term Memory,Gated Recurrent Unit,and their bi-directional and stacked variants were investigated using a real dataset collected from an EV charging station located at Trieste University(Italy).The developed forecasters have been evaluated and compared according to different metrics,including R,RMSE,MAE,and *** found that the obtained R values for both PV power generation and energy demand ranged between 97%and 98%.These study findings can be used for reliable and efficient decision-making on the management side of the optimal scheduling of the charging operations.

关键词： Microgrid electric vehicles charging station forecasting deep recurrent neural networks energy management system

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Improving Group Lasso for High-Dimensional Categorical Data 23rd

Improving Group Lasso for High-Dimensional Categorical Data

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23rd International Conference on Computational science, ICCS 2023

作者： Nowakowski, Szymon Pokarowski, Piotr Rejchel, Wojciech Soltys, Agnieszka Institute of Applied Mathematics and Mechanics University of Warsaw Warsaw Poland Faculty of Physics University of Warsaw Warsaw Poland Faculty of Mathematics and Computer Science Nicolaus Copernicus University Toruń Poland

ISBN: (纸本)9783031360206

Sparse modeling or model selection with categorical data is challenging even for a moderate number of variables, because roughly one parameter is needed to encode one category or level. The Group Lasso is a well known efficient algorithm for selection of continuous or categorical variables, but all estimates related to a selected factor usually differ. Therefore, a fitted model may not be sparse, which makes the model interpretation difficult. To obtain a sparse solution of the Group Lasso, we propose the following two-step procedure: first, we reduce data dimensionality using the Group Lasso;then, to choose the final model, we use an information criterion on a small family of models prepared by clustering levels of individual factors. In the consequence, our procedure reduces dimensionality of the Group Lasso and strongly improves interpretability of the final model. What is important, this reduction results only in the small increase of the prediction error. In the paper we investigate selection correctness of the algorithm in a sparse high-dimensional scenario. We also test our method on synthetic as well as the real data sets and show that it outperforms the state of the art algorithms with respect to the prediction accuracy, model dimension and execution time. Our procedure is contained in the R package DMRnet and available in the CRAN repository. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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'Do Not Disturb': An Implication Design To Be Alerted But Less Stress

'Do Not Disturb': An Implication Design To Be Alerted But Le...

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2023 International Seminar on Application for technology of Information and Communication, iSemantic 2023

作者： Jarupreechachan, Weerachaya Kitchat, Kotcharat Surasak, Thattapon Kasetsart University Faculty of Engineering at Kamphaeng Saen Dept. of Computer Engineering Nakhon Pathom Thailand National Central University Dept. of Computer Science and Information Engineering Taoyuan Taiwan King Mongkut's University of Technology North Bangkok Faculty of Applied Science Dept. of Computer and Information Science Bangkok Thailand

ISBN: (纸本)9798350339215

The pervasive utilization of smartphones, while augmenting personal and professional communications, also elicits mental stress and disrupts the work-life balance. Users necessitate staying connected to critical updates without undue disruption, necessitating an optimized, user-friendly smartphone interface. However, current "Do Not Disturb (DND)"features fall short of providing the desired customization options, thereby contributing to heightened anxiety and diminished productivity. Consequently, this study proposes a design modification to the DND mode by incorporating an "Exceptional Apps Notification", feature thereby aiding in alleviating stress and enhancing focus by permitting essential alerts from selected apps or contacts. In other words, the users only need to turn off notifications for specific applications, not every application. This approach addresses the problem of users fearing they will miss some important notifications. Therefore, the design process employed principles of selective attention and color theory, and user experience was assessed through questionnaires and psycho-physiological feedback. Early findings suggest the potential for stress reduction, albeit with opportunities for further optimization, such as including a memory feature for user preferences and allowing selective access for specific contacts. © 2023 IEEE.

关键词： User interfaces

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Medical Diagnoses: Heart Disease Prediction 2

Medical Diagnoses: Heart Disease Prediction

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2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Mahfuri, Mahmoud Ghazal, Taher M. Mudassar, Muhammad Siddiqui, Shahan Yamin Farooq, Sajid Kanwal, Nayab Ahmad, Munir International University for Science and Technolgy Computer and Information Technolgy Department Faculty of Engineering Syria Center for Cyber Security Faculty of Information Science and Technology Selangor Bangi43600 Malaysia Skyline University College School of Information Technology University City Sharjah 1797 United Arab Emirates The University of Lahore Lahore Pakistan Minhaj University Department of Computer Science Lahore Pakistan School of Computer Science National College of Business Administration & Economics Lahore Pakistan NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan

ISBN: (纸本)9798350335644

The successful and continuously increasing applications of machine learning (ML) and data mining (DM) in the wide range of fields such as e-businesses, public or private sectors, marketing and retails has led the ML and DM applications in other industries and health sectors. In the health sector the data is information wise too rich but not knowledge rich. It can be due to the lack of effective tools and research study in this era. In this research work, an efficient model is proposed based on the machine learning and data mining tools to predict a particular medical disease that is the heart disease. The solution for the problem which is addressed that data is not knowledge-rich, is presented in this paper. Waikato Environment and Python is used for to analyze the data, so that an automation system can be formed. Several experiments are to be implemented to compare the performance through Supervised Learning and Unsupervised Learning. Bayesian learning (Naïve Bayes), Tree's algorithm (Decision Tree), and Neural Networks (MLPNN) are applied on the same dataset. Comparatively, Decision Tree gives best accuracy when compared with Naïve Bayes, ANN performs more effective when compared with others two Bayesian algorithms (Naïve Bayes) and Tree's algorithm (Decision tree). © 2023 IEEE.

关键词： Decision trees

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