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Results in Materials 2022年 15卷

作者： Bello, Sururah Apinke Oyedele, Lukumon Olaitan, Olakunle Kazeem Olonade, Kolawole Adisa Olajumoke, Akinropo Musiliu Ajayi, Anuoluwapo Akanbi, Lukman Akinade, Olugbenga Sanni, Mistura Laide Bello, Abdul-Lateef Big Data Enterprise and Artificial Intelligence Laboratory (Big-DEAL) Bristol Business School University of the West of England Bristol United Kingdom Department of Computer Science and Engineering Obafemi Awolowo University Ile-Ife Nigeria Department of Civil Engineering University of Lagos Akoka Nigeria Department of Civil Engineering Obafemi Awolowo University Ile-Ife Nigeria Faculty of Environment and Technology University of the West of England Bristol United Kingdom Department of Computing and Games School of Computing Engineering and Digital Technologies Teeside University Middlesbrough United Kingdom

Concrete is a versatile construction material, but the water content can greatly influence its quality. However, using the trials and error method to determine the optimum water for the concrete mix results in poor quality concrete structures, which often end up in landfills as construction wastes, thus threatening environmental safety. This paper develops deep neural networks to predict the required water for a normal concrete mix. Standard data samples obtained from certified/leading laboratories were fed into a deep learning model (multilayers feedforward neural network) to automate the calibration of mixing power of the concrete water content for improved water control accuracy. We randomly split the data into 70%, 15% and 15%, respectively, to train, validate and test the model. The developed DNN model was subjected to relevant statistical metrics and benchmarked against the random forest, gradient boosting machines, and support vector machines. The performance indices obtained by the DNN model have the highest reliability compared to other models for concrete water prediction. © 2022 The Authors

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Bayesian Mixture Generalized Extreme Value Regression with Double-Exponential CAR Frailty for Dengue Haemorrhagic Fever in Pamekasan, East Java, Indonesia

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Journal of Physics: Conference Series 2021年第1期1752卷

作者： D Rantini N Iriawan Irhamah Department of Statistics Faculty of Mathematics Computing and Data Science Institut Teknologi Sepuluh Nopember Surabaya 60111 Indonesia

There is a lot of research on infectious diseases. One of the dangers is caused by dengue hemorrhagic fever (DHF) viruses. Almost all the world there are dengue viruses that spread. In health science, this research continues to increase over time. It has been widely studied by medical circles, but from a statistical point of view, patient survival can be investigated probabilistically. The survival time of DHF patients could refer to the time of arrival of DHF patients to the hospital with all the health conditions of the patient to be discharged from the hospital. It is because the patient has been declared medically cured or further improved from the initial hospital admission. In this case, survival analysis will be appropriate to be implemented, where the event of the study is DHF patients who are discharged from the hospital due to recovery. DHF patient survival data is found following the multimodal Generalized Extreme Value distribution so that a mixture model would be employed. There is a lot of transmission media for diseases transmitted by the DHF virus, but the fastest transmission medium occurs in a short time, called the Aedes Aegypti Mosquito. This transmission of the DHF virus by mosquitoes can occur between adjacent neighboring regions. This article brings together two fields of research, namely survival models with spatial random effects. The Bayesian analysis will be used as a model parameter estimation approach, whereby unmeasured autocorrelations between intersecting regions will be captured through the conditional autoregressive (CAR). Two different distributions approach for these unmeasured autocorrelations will be given, i.e. the Normal CAR and the Double Exponential CAR. Four predictor variables, namely sex, age, hematocrit level, and platelet count in each DHF patient are used to explain the variability of the survival. The Cox Proportional Hazard regression is employed to find out the significant influence of those variables on the surviva

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Optimizing Multi-Hop Quantum Communication using Bidirectional Quantum Teleportation Protocol

arXiv

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arXiv 2025年

作者： Ikken, N. Kumar, P. Slaoui, A. Kar, B. Laamara, R. Ahl Almousa, M. El-Latif, A.A. Abd LPHE-Modeling and Simulation Faculty of Sciences Mohammed V University Rabat Morocco Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Centre of Physics and Mathematics CPM Faculty of Sciences Mohammed V University in Rabat Rabat Morocco 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 Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom32511 Egypt

In this paper, we introduce a new method for Bidirectional Quantum Teleportation called Bidirectional Quantum Teleportation using the Modified Dijkstra Algorithm and Quantum Walk (BQT-MDQW). This method uses different types of entangled states, such as the GHZ-Bell state, W-Bell state, and Cluster-Bell state, to improve quantum communication in multi-hop quantum wireless networks. We focus on the W-Bell state and compare the quantum Dijkstra algorithm with the classical Dijkstra method to see which one works better. We apply both versions to quantum and classical simulators, measuring their performance through fidelity, memory utilization, and throughput calculations. Our results show that the shortest path problem may be solved with significantly reduced computer complexity using the quantum Dijkstra algorithm based on quantum walks. The introduction of a quantum walk, which permits dynamic transitions between quantum channels and the effective exploration of quantum network states, is an important part of the protocol. Using the capacity of the quantum walk to adjust to changing quantum states, we also introduce a method for successfully identifying unitary matrices under varying quantum channels. The bidirectional teleportation structure of the protocol is designed to solve the multi-hop teleportation problem in quantum wireless networks. In addition, we present quantum Dijkstra’s algorithm, which uses quantum gates to significantly decrease computational complexity and solve the networking problem by building on the quantum walk framework. This method shows how quantum computing may be used to solve arbitrary optimization issues such as the shortest path problem. Finally, we present a novel multi-hop quantum teleportation system encompassing both unidirectional and bidirectional communication, as introduced in the quantum Dijkstra algorithm system. This system significantly enhances communication in quantum networks by enabling efficient and reliable information

关键词： Quantum channel

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Channel-Annotated Deep Learning for Enhanced Interpretability in EEG-Based Seizure Detection

SSRN

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SSRN 2024年

作者： Wong, Sheng Fung Simmons, Anj Rivera-Villicana, Jessica Barnett, Scott Sivathamboo, Shobi Perucca, Piero Ge, Zongyuan Kwan, Patrick Kuhlmann, Levin O’Brien, Terence J. Applied Artificial Intelligence Institute Deakin University BurwoodVIC Australia School of Computing Technologies RMIT University Australia Department of Neuroscience Central Clinical School Monash University MelbourneVIC Australia Department of Neurology Alfred Health MelbourneVIC Australia Department of Medicine The Royal Melbourne Hospital The University of Melbourne ParkvilleVIC Australia Department of Neurology The Royal Melbourne Hospital ParkvilleVIC Australia Epilepsy Research Centre Department of Medicine Austin Health The University of Melbourne HeidelbergVIC Australia Bladin-Berkovic Comprehensive Epilepsy Program Austin Health HeidelbergVIC Australia Department of Data Science and AI Faculty of IT Monash University ClaytonVIC Australia eResearch Centre

Currently, electroencephalogram (EEG) provides critical data to support a diagnosis of epilepsy through the identification of seizure events. The review process is undertaken by clinicians or EEG specialists and is labour-intensive, especially for longterm EEG recordings. Deep learning (DL) has been proposed to automate and expedite the seizure review and annotation process, providing superior performance when compared to traditional machine learning (ML) methods. However, DL algorithms lack interpretability which is a crucial factor for clinical adoption. Consequently, the "black-box" nature of these DL algorithms limits the transparency of these algorithms, preventing clinicians from having knowledge of how the predictions are derived. In this study, we propose a novel two-block seizure detection algorithm that leverages the channel-annotation of the EEG recordings in the TUH EEG Seizure Corpus (TUSZ) based on the likelihood of seizure activities on each channel. This method allows direct interpretation of the EEG segment without requiring any further interpretability or Explainable Artificial Intelligence (XAI) methods during the prediction phase. Further, we adopted an interpretable method for explaining decisions made by the seizure detection algorithms, identifying channels that influence the final predictions. This DL approach utilizing CNN, transformer and MLP achieved an AUC of 0.92 for the seizure detection task, comparable with other state-of-the-art algorithms. Our algorithm was further validated on a separate continuous EEG dataset achieving an AUC of 0.82. Additionally, we also evaluated the reliability and efficacy of our XAI method on predicted seizure events, achieving a sensitivity of 0.59 in accurately localizing channels with seizure activities. © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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Deep Learning for Brain Age Estimation: A Systematic Review

arXiv

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arXiv 2022年

作者： Tanveer, M. Ganaie, M.A. Beheshti, Iman Goel, Tripti Ahmad, Nehal Lai, Kuan-Ting Huang, Kaizhu Zhang, Yu-Dong Ser, Javier Del Lin, Chin-Teng Department of Mathematics Indian Institute of Technology Indore Simrol Indore453552 India Department of Robotics University of Michigan Ann ArborMI48109 United States Department of Human Anatomy and Cell Science Rady Faculty of Health Sciences Max Rady College of Medicine University of Manitoba WinnipegMB Canada Biomedical Imaging Lab National Institute of Technology Assam Silchar788010 India Department of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Data Science Research Center Duke Kunshan University China School of Computing and Mathematical Sciences University of Leicester United Kingdom Derio48160 Spain Bilbao48013 Spain School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia

Over the years, Machine Learning models have been successfully employed on neuroimaging data for accurately predicting brain age. Deviations from the healthy brain aging pattern are associated to the accelerated brain aging and brain abnormalities. Hence, efficient and accurate diagnosis techniques are required for eliciting accurate brain age estimations. Several contributions have been reported in the past for this purpose, resorting to different data-driven modeling methods. Recently, deep neural networks (also referred to as deep learning) have become prevalent in manifold neuroimaging studies, including brain age estimation. In this review we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data. We detail and analyze different deep learning architectures used for this application, pausing at research works published to date quantitatively exploring their application. We also examine different brain age estimation frameworks, comparatively exposing their advantages and weaknesses. Finally, the review concludes with an outlook towards future directions that should be followed by prospective studies. The ultimate goal of this paper is to establish a common and informed reference for newcomers and experienced researchers willing to approach brain age estimation by using deep learning models. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Time-Aware Missing Traffic Flow Prediction for Sensors with Privacy-Preservation

Time-Aware Missing Traffic Flow Prediction for Sensors with ...

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作者： Lianyong Qi Fan Wang Xiaolong Xu Wanchun Dou Xuyun Zhang Mohammad R.Khosravi Xiaokang Zhou School of Computer Science Qufu Normal University School of Computer and Software Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and Technology State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Department of Computing Macquarie University Department of Computer Engineering Persian Gulf University Department of Electrical and Electronic Engineering Shiraz University of Technology Faculty of Data Science Shiga University

With the continuous development of Io T, a number of sensors establish on the roadside to monitor traffic conditions in real time. The continuously traffic data generated by these sensors makes traffic management feasible. However, loss of data may occur due to inevitable sensor failure, impeding traffic managers to understand traffic dynamics clearly. In this situation, it is becoming a necessity to predict missing traffic flow accurately for effective traffic management. Furthermore, the traffic sensor data are often distributed and stored by different agencies,which inhibits the multi-party sensor data sharing significantly due to privacy concerns. Therefore, it has become a major obstacle to balance the tradeoff between data sharing and vehicle privacy. In light of these challenges, we propose a privacyaware and accurate missing traffic flow prediction approach based on time-aware Locality-Sensitive Hashing technique. At last, we deploy a set of experiments based on a real traffic dataset. Experimental reports demonstrate the feasibility of our proposal in terms of traffic flow prediction accuracy and efficiency while guaranteeing sensor data privacy.

关键词： Traffic flow prediction Multi-party sensors Privacy Locality-Sensitive Hashing

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Plot2API: Recommending graphic API from plot via semantic parsing guided neural network

arXiv

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arXiv 2021年

作者： Wang, Zeyu Huang, Sheng Liu, Zhongxin Yan, Meng Xia, Xin Wang, Bei Yang, Dan Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Hangzhou China Faculty of Information Technology Monash University Australia Pengcheng Laboratory Shenzhen China

Plot-based Graphic API recommendation (Plot2API) is an unstudied but meaningful issue, which has several important applications in the context of software engineering and data visualization, such as the plotting guidance of the beginner, graphic API correlation analysis, and code conversion for plotting. Plot2API is a very challenging task, since each plot is often associated with multiple APIs and the appearances of the graphics drawn by the same API can be extremely varied due to the different settings of the parameters. Additionally, the samples of different APIs also suffer from extremely imbalanced. Considering the lack of technologies in Plot2API, we present a novel deep multi-task learning approach named Semantic Parsing Guided Neural Network (SPGNN) which translates the Plot2API issue as a multi-label image classification and an image semantic parsing tasks for the solution. In SPGNN, the recently advanced Convolutional Neural Network (CNN) named EfficientNet is employed as the backbone network for API recommendation. Meanwhile, a semantic parsing module is complemented to exploit the semantic relevant visual information in feature learning and eliminate the appearance-relevant visual information which may confuse the visual-information-based API recommendation. Moreover, the recent data augmentation technique named random erasing is also applied for alleviating the imbalance of API categories. We collect plots with the graphic APIs used to drawn them from Stack Overflow, and release three new Plot2API datasets corresponding to the graphic APIs of R and Python programming languages for evaluating the effectiveness of Plot2API techniques. Extensive experimental results not only demonstrate the superiority of our method over the recent deep learning baselines but also show the practicability of our method in the recommendation of graphic APIs. © 2021, CC BY-NC-SA.

关键词： data visualization

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An economic production quantity (EPQ) model with rework process and reworked items sold in secondary markets

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AIP Conference Proceedings 2024年第1期3016卷

作者： Soo Lin Lee I. Yeo Wan Yoke Chin Department of Mathematical and Data Science Faculty of Computing and Information Technology Tunku Abdul Rahman University of Management and Technology Kuala Lumpur Main Campus Jalan Genting Kelang Setapak 53300 Kuala Lumpur Malaysia Lee Kong Chian Faculty of Engineering and Science Department of Mathematical and Actuarial Sciences Tunku Abdul Rahman University Sungai Long Campus Jalan Sungai Long Bandar Sungai Long Cheras 43000 Kajang Selangor Malaysia

A sustainable inventory system aims to increase the quality of its production and reduce its impact on the environment without increasing its costs. This study presents an economic production quantity (EPQ) model with a rework process where the production of good quality items is used to fulfil primary market demand, whereas the reworked defective items are used to fulfil secondary market demand. There are five cases being observed during the simultaneous production process and reworking process, and a generalised model that minimises the total cost per unit time (TCUT) is developed. The proposed model is experimented with using a variety of numerical examples, and a numerical sensitivity analysis that studies how different system parameters affect costs is performed. This analysis suggests that the proposed model is fairly robust to changes in parameters; however, the higher the proportion of defective items in a production lot size, the more serious the impact on the optimal cost. The results show that the TCUT is increasing exponentially with respect to the proportion of defective items in a production lot size.

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Optimizing Scene Classification: A Robust Approach with Transfer Learning and Automated Machine Learning Integration

Journal of Engineering Research

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Journal of Engineering Research 2025年

作者： Mohamed Hammad Samia A. Chelloug Samah AlShathri Ahmed A. Abd El-Latif Department of Information Technology Faculty of Computers and Information Menoufia University Shibin El Kom 32511 Egypt EIAS Data Science Lab College of Computer and Information Sciences and Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh 11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom 32511 Egypt

Scene classification is a fundamental challenge in computer vision. Recognizing the complexity of this task is the aim of our study that addresses the need for accurate and robust scene classification by leveraging the capabilities of two widely recognized databases. The motivation behind this research lies in enhancing the accuracy and efficiency of scene classification systems. Therefore, our primary goal is to explore and implement a comprehensive methodology that combines transfer learning and automated machine learning techniques to achieve superior classification results. Our approach commences with a meticulous data loading process, followed by preprocessing steps to ensure the optimal representation of information. We have conducted class distribution analysis to understand the dataset's nuances. Subsequently, we have employed two key models: MobileNetV2 for transfer learning and a custom convolutional neural network (CNN) model featuring batch normalization. This diverse methodology aims to capture intricate patterns within the data. An innovative step of our approach involves employing Tree-based Pipeline Optimization Tool (TPOT), an automated machine learning tool, for model selection and hyperparameter tuning. The results underscore the effectiveness of our methodology, achieving impressive classification accuracy across diverse scenes. This research contributes valuable insights into the integration of transfer learning and automated machine learning for robust and accurate scene recognition, offering a comprehensive approach to address the complexities of scene classification.

关键词： CNN Scene Recognition TPOT MobileNetV2 Transfer Learning Machine Learning

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Multi-objective optimization of unbalanced power distribution systems: a comprehensive approach to address uncertainties and enhance performance

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Energy Conversion and Management: X 2025年 27卷

作者： Mohammad Reza Maghami Elaheh Yaghoubi Mazlan Mohamed Elnaz Yaghoubi Mehdi Zareian Jahromi Thang Ka Fei Strategic Research Institute (SRI) Asia Pacific University of Technology and Innovation (APU) Malaysia Faculty of Data Science and Computing University Kelantan Malaysia Malaysia Department of Electrical and Electronics Engineering Karabuk University Turkey Department of Electrical and Electronics Engineering Amirkabir University Iran School of Technology Asia Pacific University of Technology and Innovation (APU) Malaysia

This study presents an advanced multi-objective optimization framework to enhance the performance of unbalanced power distribution systems. It highlights the significance of three-phase load flow analysis and addresses key problems such as voltage fluctuations, power losses, cost inefficiency, and uncertainty related to renewable energy integration. A holistic strategy integrating demand response (DR), energy storage, and distributed generation is employed. Using a customized Genetic Algorithm (GA), the model optimizes the capacity, location, and phase placement of energy sources, including solar, wind, biomass, diesel, and utility power. It also incorporates probabilistic scenario generation to manage uncertainties in load and renewable resources. Simulation results on the IEEE 13-bus system demonstrate substantial improvements: 91 % reduction in power losses (from 9924 kW to 780 kW), 11 % reduction in load profiles, 60 % decrease in utility usage, and 84 % reduction in overall network cost. These findings confirm the effectiveness of the proposed framework in enhancing energy efficiency, reliability, and sustainability in modern unbalanced distribution networks.

关键词： Three phase load flow Unbalance network Muti-objective optimization Three phase network

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