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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Hegde, Sandeep Kumar Hegde, Rajalaxmi Krishna Priya, R. Sree Southry, S. Marthanda, A.V.G.A. Logu, K. Karkala Taluk Udupi India Department of Engineering Electrical Section University of Technology and Applied Sciences Shinas campus PO Box 66Al-Aqur Shinas324 Oman Department of ECE Sona College of Technology Salem India Department of EEE Laki Reddy Bali Reddy College of Engineering Mylavaram Permanently Affiliated to JNTU Andhrapradesh Kakinada India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India

ISBN: (纸本)9781665464086

The banking sector has advanced in recent years. Thus, there is an increase in the demand for bank loans. The bank must distribute and sell each of the limited number of available slots to a select group of people. As a result, a usual stage is to identify who will be unable to return the loan and who will prove to be a more trustworthy option to the bank. In order to save the bank time and costs, in the proposed paper machine learning based approach is introduced to reduce the risk involved with finding the safe individual. In order to decide whether or not to grant someone a loan, this paper presents a method of loan approval based on predetermined criteria. The machine learning model for credit approval was implemented using logistic regression, XG Boost, random forest and naïve bayes model. The experimental results indicates that logistic regression model is more accurate for the credit risk analysis. © 2023 IEEE.

关键词： Python

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Biphoton spectral quantum interference for information processing and delay metrology 3

Biphoton spectral quantum interference for information proce...

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Quantum Computing, Communication, and Simulation III 2023

作者： Seshadri, Suparna Lu, Hsuan-Hao Lukens, Joseph M. Weiner, Andrew M. School of Electrical and Computer Engineering Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Quantum Information Science Section Oak Ridge National Laboratory Oak RidgeTN37831 United States Research Technology Office Arizona State University TempeAZ85287 United States

ISBN: (纸本)9781510659971

Broadband time-energy entangled photons feature strong temporal correlations with potential for precision delay metrology, but previous work has leveraged only time-of-flight information ultimately limited by the detection jitter and resolution of the time-tagging electronics. Firstly, our work pushes the entanglement-based nonlocal delay metrology from the conventional time-of-flight measurement to a new direction—two-photon interferometry with subpicosecond sensitivity independent of detection resolution. Next, we show the selective sensitivity of frequency-bin encoded Bell states to the sum and difference of biphoton-delays by using a novel reconfigurable setup capable of switching between the Bell states by successively employing single and dual spectral-line pumps. © 2023 SPIE.

关键词： Photons

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Centroidal-Polygon: Solving First Order Ordinary Differential Equation using Centroidal mean to improve Euler Method 1

Centroidal-Polygon: Solving First Order Ordinary Differentia...

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1st International Recent Trends in engineering, Advanced Computing and technology Conference, RETREAT 2020

作者： Nur Shahirah, Z. Nooraida, S. Wan Farah Hanan, W.A. Suzanna, R.A. Iliana, M.A. Section of Instrumentation Technology Faculty of Computer Media and Technology Management Faculty of Engineering Tati University College Teluk Kalong Kemaman Terengganu Malaysia

Euler method is one of the oldest and simplest numerical techniques to solve mathematical problems. However, the Euler method lacks accuracy compared to other methods such as the Runge-Kutta and the Adam Bashforth. This research proposed a new scheme by enhancing the Euler method using centroidal mean, i.e. combining the Euler method and the mean concept. In this research, the proposed scheme would be used to improve the accuracy of the first order Ordinary Differential Equation (ODE). The enhanced Euler method was used based on the improved Euler method, which is a midpoint method. To analyse the accuracy of the proposed scheme and the existing schemes, SCILAB 6.0 software was used to compare the results of different step sizes. This research compared the proposed scheme with the Polygon scheme by Zul Zamri and the Harmonic-Polygon scheme by Nurhafizah. By using Centroidal-Polygon as the proposed scheme, more accurate results were yielded. However, the scheme was limited to some of the linear and non-linear equations only. © Published under licence by IOP Publishing Ltd.

关键词： Ordinary differential equations

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The effect of task processing management on energy consumption at the edge of Internet of things network with using reinforcement learning method

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computers and Industrial engineering 2024年 195卷

作者： Mohammadian, Asghar Zarrabi, Houman Jabbehdari, Sam Masoud Rahmani, Amir Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Tehran Iran Department of Computer Engineering North Tehran Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan

In this paper, the task processing approach at the edge of the Internet of Things (IoT) network in improving energy consumption was investigated. First, the subject of maximizing the use of energy is defined as the main problem and then based on the state of assignment of task processing from end devices at the edge of the network to smart gateways and local servers at the edge using possible low bandwidth and low battery consumption, the problem is divided into two subject which are obtained independently. After modeling by recombining these two sub-problems, the subject of maximizing the utility gain and battery life of the end devices is solved despite the limitations of processing time and energy resources. Considering that the environment of the problem and how the terminal devices are connected with the edge devices are unknown, a repetitive reinforcement learning algorithm has been used to create an optimal solution to maximize the operational benefit of the energy consumed in edge processing. The results achieved from the simulation show the increase in network load and processing overhead with the increase in the number of active devices. The suggested method, while increasing the edge processing speed, decreases the delay and maximizes the performance gain of the IOT edge system compared to the central cloud system. It should be noted that when the number of active end devices dramatically increases, the energy consumption and bandwidth at the network edge improves, and energy consumption at the network edge decreases lonely by 12.5%. © 2024 Elsevier Ltd

关键词： Energy utilization

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A Novel Explainable CNN Model for Screening COVID-19 on X-ray Images

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computer Systems Science & engineering 2023年第8期46卷 1789-1809页

作者： Hicham Moujahid Bouchaib Cherradi Oussama El Gannour Wamda Nagmeldin Abdelzahir Abdelmaboud Mohammed Al-Sarem Lhoussain Bahatti Faisal Saeed Mohammed Hadwan EEIS Laboratory ENSET of MohammediaHassan II University of CasablancaMohammedia28820Morocco STIE Team CRMEF Casablanca-SettatThe Provincial Section of El JadidaEl Jadida24000Morocco Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Information Systems King Khalid UniversityMuhayel Aseer61913Saudi Arabia College of Computer Science and Engineering Taibah UniversityMedina42353Saudi Arabia Department of Computer Science University of Sheba RegionMarib14400Yemen DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City UniversityBirminghamB47XGUK Department of Information Technology College of ComputerQassim UniversityBuraydah 51452Saudi Arabia Department of Computer Science College of Applied SciencesTaiz UniversityTaiz6803Yemen

Due to the rapid propagation characteristic of the Coronavirus(COVID-19)disease,manual diagnostic methods cannot handle the large number of infected individuals to prevent the spread of ***,new automated diagnostic methods have been brought on board,particularly methods based on artificial intelligence using different medical data such as X-ray *** imaging,for example,produces several image types that can be processed and analyzed by machine and deep learning methods.X-ray imaging materials widely exist in most hospitals and health institutes since they are affordable compared to other imaging *** this paper,we propose a novel Convolutional Neural Network(CNN)model(COV2Net)that can detect COVID-19 virus by analyzing the X-ray images of suspected *** model is trained on a dataset containing thousands of X-ray images collected from different *** model was tested and evaluated on an independent *** order to approve the performance of the proposed model,three CNN models namely Mobile-Net,Residential Energy Services Network(Res-Net),and Visual Geometry Group 16(VGG-16)have been implemented using transfer learning *** experiment consists of a multi-label classification task based on X-ray images for normal patients,patients infected by COVID-19 virus and other patients infected with *** proposed model is empowered with Gradient-weighted Class Activation Mapping(Grad-CAM)and Grad-Cam++techniques for a visual explanation and methodology debugging *** finding results show that the proposed model COV2Net outperforms the state-of-the-art methods.

关键词： Artificial intelligence intelligent diagnostic systems decisionmaking COVID-19 convolutional neural network

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Integration of FDOSM and FWZIC under Homogeneous Fermatean Fuzzy Environment: A Prioritization of COVID-19 Patients for Mesenchymal Stem Cell Transfusion

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International Journal of Information technology and Decision Making 2024年第4期23卷 1559-1599页

作者： Alsattar, H.A. Qahtan, Sara Mohammed, R.T. Zaidan, A.A. Albahri, O.S. Kou, Gang Alamoodi, A.H. Albahri, A.S. Zaidan, B.B. Al-Samarraay, Mohammed S. Malik, R.Q. Jasim, Ali Najm Department of Computing Faculty of Arts Computing and Creative Industry Universiti Pendidikan Sultan Idris Tanjung Malim35900 Malaysia Department of Business Administration College of Administrative Science The University of Mashreq Baghdad10021 Iraq Department of Computer Center Middle Technical University's Baghdad Iraq Sulaymaniyah Iraq Faculty of Engineering and IT The British University in Dubia United Arab Emirates Computer Techniques Engineering Department Mazaya University College Nassiriya Thi-Qar Iraq School of Business Administration Faculty of Business Administration Southwestern University of Finance and Economics Chengdu611130 China Baghdad Iraq Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University College Babylon Iraq Foundation of Alshuhda Baghdad Iraq

Mesenchymal stem cell (MSC) transfusion has shown promising results in treating COVID-19 cases despite the limited availability of these MSCs. The task of prioritizing COVID-19 patients for MSC transfusion based on multiple criteria is considered a multi-Attribute decision-Analysis (MADA) problem. Although literature reviews have assessed the prioritization of COVID-19 patients for MSCs, issues arising from imprecise, unclear and ambiguous information remain unresolved. Compared with the existing MADA methods, the robustness of the fuzzy decision by opinion score method (FDOSM) and fuzzy-weighted zero inconsistency (FWZIC) is proven. This study adopts and integrates FDOSM and FWZIC in a homogeneous Fermatean fuzzy environment for criterion weighting followed by the prioritization of the most eligible COVID-19 patients for MSC transfusion. The research methodology had two phases. The decision matrices of three COVID-19 emergency levels (moderate, severe, and critical) were adopted based on an augmented dataset of 60 patients and discussed in the first phase. The second phase was divided into two subsections. The first section developed Fermatean FWZIC (F-FWZIC) to weigh criteria across each emergency level of COVID-19 patients. These weights were fed to the second section on adopting Fermatean FDOSM (F-FDOSM) for the purpose of prioritizing COVID-19 patients who are the most eligible to receive MSCs. Three methods were used in evaluating the proposed works, and the results included systematic ranking, sensitivity analysis, and benchmarking checklist. © 2024 World Scientific Publishing Company.

关键词： COVID-19

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Generalized fractional optimization-based explainable lightweight CNN model for malaria disease classification

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computers in Biology and Medicine 2025年 185卷 109593-109593页

作者： Khan, Zeshan Aslam Waqar, Muhammad Raja, Muhammad Junaid Ali Asif Chaudhary, Naveed Ishtiaq Khan, Abeer Tahir Mehmood Anwar Raja, Muhammad Asif Zahoor International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Section 3 123 University Road Douliou Yunlin64002 Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Section 3 123 University Road Douliou Yunlin64002 Taiwan Future Technology Research Center National Yunlin University of Science and Technology Section 3 123 University Road Douliou Yunlin64002 Taiwan Rawal Institute of Health Sciences Islamabad Pakistan

Over the past few decades, machine learning and deep learning (DL) have incredibly influenced a broader range of scientific disciplines. DL-based strategies have displayed superior performance in image processing compared to conventional standard methods, especially in healthcare settings. Among the biggest threats to global public health is the fast spread of malaria. The plasmodium falciparum infection, the disease origin causes the intestinal illness. Fortunately, advances in artificial intelligence techniques have made it possible to use visual data sets to quickly and effectively diagnose malaria which has also proven to be cost and time effective. In literature, several DL approaches have previously been used with good precision but suffer from computational inefficiency and interpretability. Therefore, this research proposes a generalized fractional order-based explainable lightweight convolutional neural network model to overcome these limitations. The fractional order optimization algorithms have proven worth in terms of estimation accuracy and convergence speed for different applications. The proposed fractional order optimizer-based model offers an improved solution to malaria disease diagnosis with a percentage accuracy of 95 % using the standard NIH dataset and outperforms the existing complex models concerning speed and effectiveness. The proposed fractionally optimized lightweight CNN model has shown substantial performance on the external MP-IDB dataset and M5 test set as well by achieving a generalized test accuracy of 92 % and 90.4 % which verifies the robustness and generalizability of the proposed solution under available circumstances. Moreover, the efficacy of the proposed lightweight architecture is endorsed through evaluation metrics of precision, recall, and F1-score. © 2024 Elsevier Ltd

关键词： Convolutional neural networks

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Understanding crowd energy consumption behaviors 26

Understanding crowd energy consumption behaviors

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26th International Conference on Extending Database technology, EDBT 2023

作者： Liu, Xiufeng Cheng, Xu Yang, Yanyan Huo, Huan Liu, Yongpin Nielsen, Per Sieverts Department of Technology Management and Economics Technical University of Denmark Germany Section of Energy Markets Smart Innovation Norway Halden Norway School of Computing University of Portsmouth PortsmouthPO1 3HE United Kingdom School of Computer Science University of Technology Sydney Australia Department of Manufacturing and Civil Engineering Norwegian University of Science and Technology Gjøvik Norway

ISBN: (纸本)9783893180929

Understanding crowd behavior is crucial for energy demand-side management. In this paper, we employ the fluid dynamics concept potential flow to model the energy demand shift patterns of the crowd in both temporal and spatial dimensions. To facilitate the use of the proposed method, we implement a visual analysis platform that allows users to interactively explore and interpret the shift patterns. The effectiveness of the proposed method will be evaluated through a hands-on experience with a real case study during the conference demonstration. © 2023 Copyright held by the owner/author(s)

关键词： Behavioral research

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TransNILM: A Transformer-based Deep Learning Model for Non-intrusive Load Monitoring 4

TransNILM: A Transformer-based Deep Learning Model for Non-i...

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4th International Conference on High Performance Big Data and Intelligent Systems, HDIS 2022

作者： Cheng, Xu Zhao, Meng Zhang, Jianhua Wang, Jinghao Pan, Xueping Liu, Xiufeng Section of Energy Markets Smart Innovation Norway Halden Norway Tianjin University of Technology School of Computer Science and Engineering Tianjin China Norwegian University of Science and Technology Department of Electric Power Engineering Trondheim Norway College of Energy and Electrical Engineering HoHai University Nanjing China Technical University of Denmark Lyngby Department of Technology Management and Economics Denmark

ISBN: (数字)9781665491440

ISBN: (纸本)9781665491440

Non-intrusive load monitoring (NILM) is crucial for energy efficiency in demand-side management. The conventional methods, such as combination optimization or the factorial hidden Markov model, are not effective to obtain the energy consumption information of each appliance. To overcome the limitations of these conventional methods, deep learning-based methods are widely developed. Inspired by recent advances in deep learning models, this paper proposes a novel transformer-based deep learning model for NILM. In this proposed model, transformer-layer enhanced convolutional neural networks (CNN) are firstly utilized for feature learning, a temporal scaling module is then developed for multi-scale information learning. Finally, these learned features are fed into a decoder which consists of three residual GRU modules and a transformer-based CNN module. To evaluate the proposed method, this paper conducts experiments on one residential dataset and one commercial dataset and compares our proposed model with several state-of-The-Art methods. The experimental results demonstrate that the proposed method achieves better performance and has good generalization capability. © 2022 IEEE.

关键词： Hidden Markov models

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Incorporating Onboard Visual Sensing and Geographic Information for Lane-Level Vehicle Localization

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IEEE Sensors Journal 2025年

作者： Lin, Huei-Yung Li, Jun-Yi Su, Ming-Wei National Taipei University of Technology Department of Computer Science and Information Engineering No. 1 Section 3 Zhongxiao East Road Taipei10608 Taiwan National Chung Cheng University Department of Electrical Engineering 168 University Road Chiayi621 Taiwan

With the recent advances in deep learning and artificial intelligence, vehicular technologies are progressing rapidly toward fully autonomous driving. Critical components of advanced driver assistance systems (ADAS) have been extensively investigated in the past decade. In this paper, we address the challenge of precise vehicle localization using visual perception and geographic information. The proposed method incorporates drivable area detection, lane line detection, and image-map matching to achieve lane-level localization accuracy. It takes advantage of digital maps and images from the onboard cameras to simultaneously optimize vehicle localization and lane detection. A confidence estimation is proposed to generate virtual lane lines for the lane marking identification. Our networks are pre-trained on the Cityscapes, CULane and TuSimple datasets, and fine-tuned with the newly proposed TRoad and TLane. In the experiments, the performance evaluated using MAE and RMSE have demonstrated a significant improvement over the conventional GPS/GNSS-based vehicle localization techniques. Source code and datasets are available at https://***/Wally0924/Enhancing-Vehicle-Localization-Using-Lane-Detection-and-Geographic-Information. © 2001-2012 IEEE.

关键词： Advanced driver assistance systems

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